[{"doi":"10.1007/978-3-030-76657-3_10","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","department":[{"_id":"HeEd"}],"day":"16","project":[{"_id":"266A2E9E-B435-11E9-9278-68D0E5697425","grant_number":"788183","call_identifier":"H2020","name":"Alpha Shape Theory Extended"},{"name":"Persistence and stability of geometric complexes","call_identifier":"FWF","grant_number":"I02979-N35","_id":"2561EBF4-B435-11E9-9278-68D0E5697425"}],"publication_status":"published","status":"public","intvolume":"     12708","scopus_import":"1","external_id":{"isi":["001286400400010"]},"date_created":"2021-08-08T22:01:29Z","volume":12708,"abstract":[{"lang":"eng","text":"We define a new compact coordinate system in which each integer triplet addresses a voxel in the BCC grid, and we investigate some of its properties. We propose a characterization of 3D discrete analytical planes with their topological features (in the Cartesian and in the new coordinate system) such as the interrelation between the thickness of the plane and the separability constraint we aim to obtain."}],"oa_version":"None","publisher":"Springer Nature","language":[{"iso":"eng"}],"month":"05","_id":"9824","page":"152-163","date_updated":"2026-04-16T09:26:30Z","citation":{"ista":"Čomić L, Zrour R, Largeteau-Skapin G, Biswas R, Andres E. 2021. Body centered cubic grid - coordinate system and discrete analytical plane definition. Discrete Geometry and Mathematical Morphology. DGMM: International Conference on Discrete Geometry and Mathematical Morphology, LNCS, vol. 12708, 152–163.","short":"L. Čomić, R. Zrour, G. Largeteau-Skapin, R. Biswas, E. Andres, in:, Discrete Geometry and Mathematical Morphology, Springer Nature, 2021, pp. 152–163.","apa":"Čomić, L., Zrour, R., Largeteau-Skapin, G., Biswas, R., &#38; Andres, E. (2021). Body centered cubic grid - coordinate system and discrete analytical plane definition. In <i>Discrete Geometry and Mathematical Morphology</i> (Vol. 12708, pp. 152–163). Uppsala, Sweden: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-76657-3_10\">https://doi.org/10.1007/978-3-030-76657-3_10</a>","ieee":"L. Čomić, R. Zrour, G. Largeteau-Skapin, R. Biswas, and E. Andres, “Body centered cubic grid - coordinate system and discrete analytical plane definition,” in <i>Discrete Geometry and Mathematical Morphology</i>, Uppsala, Sweden, 2021, vol. 12708, pp. 152–163.","mla":"Čomić, Lidija, et al. “Body Centered Cubic Grid - Coordinate System and Discrete Analytical Plane Definition.” <i>Discrete Geometry and Mathematical Morphology</i>, vol. 12708, Springer Nature, 2021, pp. 152–63, doi:<a href=\"https://doi.org/10.1007/978-3-030-76657-3_10\">10.1007/978-3-030-76657-3_10</a>.","ama":"Čomić L, Zrour R, Largeteau-Skapin G, Biswas R, Andres E. Body centered cubic grid - coordinate system and discrete analytical plane definition. In: <i>Discrete Geometry and Mathematical Morphology</i>. Vol 12708. Springer Nature; 2021:152-163. doi:<a href=\"https://doi.org/10.1007/978-3-030-76657-3_10\">10.1007/978-3-030-76657-3_10</a>","chicago":"Čomić, Lidija, Rita Zrour, Gaëlle Largeteau-Skapin, Ranita Biswas, and Eric Andres. “Body Centered Cubic Grid - Coordinate System and Discrete Analytical Plane Definition.” In <i>Discrete Geometry and Mathematical Morphology</i>, 12708:152–63. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-76657-3_10\">https://doi.org/10.1007/978-3-030-76657-3_10</a>."},"conference":{"name":"DGMM: International Conference on Discrete Geometry and Mathematical Morphology","start_date":"2021-05-24","location":"Uppsala, Sweden","end_date":"2021-05-27"},"year":"2021","acknowledgement":"This work has been partially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia through the project no. 451-03-68/2020-14/200156: “Innovative scientific and artistic research from the FTS (activity) domain” (LČ), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, grant no. 788183 (RB), and the DFG Collaborative Research Center TRR 109, ‘Discretization in Geometry and Dynamics’, Austrian Science Fund (FWF), grant no. I 02979-N35 (RB).","article_processing_charge":"No","publication_identifier":{"isbn":["9783030766566"],"issn":["0302-9743"],"eissn":["1611-3349"]},"quality_controlled":"1","publication":"Discrete Geometry and Mathematical Morphology","date_published":"2021-05-16T00:00:00Z","type":"conference","isi":1,"ec_funded":1,"alternative_title":["LNCS"],"title":"Body centered cubic grid - coordinate system and discrete analytical plane definition","author":[{"last_name":"Čomić","first_name":"Lidija","full_name":"Čomić, Lidija"},{"full_name":"Zrour, Rita","last_name":"Zrour","first_name":"Rita"},{"full_name":"Largeteau-Skapin, Gaëlle","first_name":"Gaëlle","last_name":"Largeteau-Skapin"},{"full_name":"Biswas, Ranita","id":"3C2B033E-F248-11E8-B48F-1D18A9856A87","first_name":"Ranita","last_name":"Biswas","orcid":"0000-0002-5372-7890"},{"last_name":"Andres","first_name":"Eric","full_name":"Andres, Eric"}]},{"status":"public","publication_status":"published","project":[{"name":"Teaching Old Crypto New Tricks","grant_number":"682815","_id":"258AA5B2-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"day":"23","doi":"10.15479/at:ista:10035","department":[{"_id":"GradSch"},{"_id":"KrPi"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","oa":1,"file_date_updated":"2022-03-10T12:15:18Z","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"degree_awarded":"PhD","supervisor":[{"full_name":"Pietrzak, Krzysztof Z","last_name":"Pietrzak","first_name":"Krzysztof Z","id":"3E04A7AA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-9139-1654"}],"oa_version":"Published Version","related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"10044"},{"id":"10048","relation":"part_of_dissertation","status":"public"},{"status":"public","relation":"part_of_dissertation","id":"10041"},{"status":"public","id":"10049","relation":"part_of_dissertation"},{"status":"public","id":"637","relation":"part_of_dissertation"},{"relation":"part_of_dissertation","id":"6430","status":"public"}]},"abstract":[{"text":"Many security definitions come in two flavors: a stronger “adaptive” flavor, where the adversary can arbitrarily make various choices during the course of the attack, and a weaker “selective” flavor where the adversary must commit to some or all of their choices a-priori. For example, in the context of identity-based encryption, selective security requires the adversary to decide on the identity of the attacked party at the very beginning of the game whereas adaptive security allows the attacker to first see the master public key and some secret keys before making this choice. Often, it appears to be much easier to achieve selective security than it is to achieve adaptive security. A series of several recent works shows how to cleverly achieve adaptive security in several such scenarios including generalized selective decryption [Pan07][FJP15], constrained PRFs [FKPR14], and Yao’s garbled circuits [JW16]. Although the above works expressed vague intuition that they share a common technique, the connection was never made precise. In this work we present a new framework (published at Crypto ’17 [JKK+17a]) that connects all of these works and allows us to present them in a unified and simplified fashion. Having the framework in place, we show how to achieve adaptive security for proxy re-encryption schemes (published at PKC ’19 [FKKP19]) and provide the first adaptive security proofs for continuous group key agreement protocols (published at S&P ’21 [KPW+21]). Questioning optimality of our framework, we then show that currently used proof techniques cannot lead to significantly better security guarantees for \"graph-building\" games (published at TCC ’21 [KKPW21a]). These games cover generalized selective decryption, as well as the security of prominent constructions for constrained PRFs, continuous group key agreement, and proxy re-encryption. Finally, we revisit the adaptive security of Yao’s garbled circuits and extend the analysis of Jafargholi and Wichs in two directions: While they prove adaptive security only for a modified construction with increased online complexity, we provide the first positive results for the original construction by Yao (published at TCC ’21 [KKP21a]). On the negative side, we prove that the results of Jafargholi and Wichs are essentially optimal by showing that no black-box reduction can provide a significantly better security bound (published at Crypto ’21 [KKPW21c]).","lang":"eng"}],"date_created":"2021-09-23T07:31:44Z","month":"09","page":"276","_id":"10035","date_updated":"2026-04-16T09:52:03Z","citation":{"chicago":"Klein, Karen. “On the Adaptive Security of Graph-Based Games.” Institute of Science and Technology Austria, 2021. <a href=\"https://doi.org/10.15479/at:ista:10035\">https://doi.org/10.15479/at:ista:10035</a>.","ama":"Klein K. On the adaptive security of graph-based games. 2021. doi:<a href=\"https://doi.org/10.15479/at:ista:10035\">10.15479/at:ista:10035</a>","mla":"Klein, Karen. <i>On the Adaptive Security of Graph-Based Games</i>. Institute of Science and Technology Austria, 2021, doi:<a href=\"https://doi.org/10.15479/at:ista:10035\">10.15479/at:ista:10035</a>.","ieee":"K. Klein, “On the adaptive security of graph-based games,” Institute of Science and Technology Austria, 2021.","short":"K. Klein, On the Adaptive Security of Graph-Based Games, Institute of Science and Technology Austria, 2021.","apa":"Klein, K. (2021). <i>On the adaptive security of graph-based games</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:10035\">https://doi.org/10.15479/at:ista:10035</a>","ista":"Klein K. 2021. On the adaptive security of graph-based games. Institute of Science and Technology Austria."},"corr_author":"1","publisher":"Institute of Science and Technology Austria","language":[{"iso":"eng"}],"file":[{"file_name":"thesis_pdfa.pdf","date_created":"2021-10-04T12:22:33Z","checksum":"73a44345c683e81f3e765efbf86fdcc5","file_size":2104726,"file_id":"10082","creator":"cchlebak","content_type":"application/pdf","relation":"main_file","date_updated":"2021-10-04T12:22:33Z","access_level":"open_access","success":1},{"file_name":"thesis_final (1).zip","checksum":"7b80df30a0e686c3ef6a56d4e1c59e29","file_size":9538359,"date_created":"2021-10-05T07:04:37Z","file_id":"10085","creator":"cchlebak","content_type":"application/x-zip-compressed","relation":"source_file","access_level":"closed","date_updated":"2022-03-10T12:15:18Z"}],"OA_place":"publisher","acknowledgement":"I want to acknowledge the funding by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (682815 - TOCNeT).\r\n","year":"2021","ddc":["519"],"publication_identifier":{"issn":["2663-337X"]},"article_processing_charge":"No","title":"On the adaptive security of graph-based games","alternative_title":["ISTA Thesis"],"author":[{"last_name":"Klein","first_name":"Karen","id":"3E83A2F8-F248-11E8-B48F-1D18A9856A87","full_name":"Klein, Karen"}],"ec_funded":1,"type":"dissertation","has_accepted_license":"1","date_published":"2021-09-23T00:00:00Z"},{"language":[{"iso":"eng"}],"publisher":"American Mathematical Society","_id":"10071","date_updated":"2026-06-18T08:36:13Z","page":"1511-1514","citation":{"short":"H. Adams, H. Kourimska, T. Heiss, S. Percival, L. Ziegelmeier, Notices of the American Mathematical Society 68 (2021) 1511–1514.","apa":"Adams, H., Kourimska, H., Heiss, T., Percival, S., &#38; Ziegelmeier, L. (2021). How to tutorial-a-thon. <i>Notices of the American Mathematical Society</i>. American Mathematical Society. <a href=\"https://doi.org/10.1090/noti2349\">https://doi.org/10.1090/noti2349</a>","ista":"Adams H, Kourimska H, Heiss T, Percival S, Ziegelmeier L. 2021. How to tutorial-a-thon. Notices of the American Mathematical Society. 68(9), 1511–1514.","ama":"Adams H, Kourimska H, Heiss T, Percival S, Ziegelmeier L. How to tutorial-a-thon. <i>Notices of the American Mathematical Society</i>. 2021;68(9):1511-1514. doi:<a href=\"https://doi.org/10.1090/noti2349\">10.1090/noti2349</a>","mla":"Adams, Henry, et al. “How to Tutorial-a-Thon.” <i>Notices of the American Mathematical Society</i>, vol. 68, no. 9, American Mathematical Society, 2021, pp. 1511–14, doi:<a href=\"https://doi.org/10.1090/noti2349\">10.1090/noti2349</a>.","ieee":"H. Adams, H. Kourimska, T. Heiss, S. Percival, and L. Ziegelmeier, “How to tutorial-a-thon,” <i>Notices of the American Mathematical Society</i>, vol. 68, no. 9. American Mathematical Society, pp. 1511–1514, 2021.","chicago":"Adams, Henry, Hana Kourimska, Teresa Heiss, Sarah Percival, and Lori Ziegelmeier. “How to Tutorial-a-Thon.” <i>Notices of the American Mathematical Society</i>. American Mathematical Society, 2021. <a href=\"https://doi.org/10.1090/noti2349\">https://doi.org/10.1090/noti2349</a>."},"month":"10","year":"2021","article_processing_charge":"No","quality_controlled":"1","publication_identifier":{"eissn":["1088-9477"],"issn":["0002-9920"]},"publication":"Notices of the American Mathematical Society","ddc":["500"],"date_published":"2021-10-01T00:00:00Z","type":"journal_article","author":[{"first_name":"Henry","last_name":"Adams","full_name":"Adams, Henry"},{"orcid":"0000-0001-7841-0091","full_name":"Kourimska, Hana","last_name":"Kourimska","first_name":"Hana","id":"D9B8E14C-3C26-11EA-98F5-1F833DDC885E"},{"id":"4879BB4E-F248-11E8-B48F-1D18A9856A87","first_name":"Teresa","last_name":"Heiss","full_name":"Heiss, Teresa","orcid":"0000-0002-1780-2689"},{"full_name":"Percival, Sarah","last_name":"Percival","first_name":"Sarah"},{"last_name":"Ziegelmeier","first_name":"Lori","full_name":"Ziegelmeier, Lori"}],"title":"How to tutorial-a-thon","alternative_title":["Early Career"],"department":[{"_id":"HeEd"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.1090/noti2349","day":"01","publication_status":"published","status":"public","main_file_link":[{"url":"http://www.ams.org/notices/","open_access":"1"}],"oa":1,"issue":"9","intvolume":"        68","scopus_import":"1","article_type":"letter_note","date_created":"2021-10-03T22:01:22Z","volume":68,"oa_version":"Published Version"},{"title":"Dissecting organismal morphogenesis by bridging genetics and biophysics","author":[{"last_name":"Mishra","first_name":"Nikhil","id":"C4D70E82-1081-11EA-B3ED-9A4C3DDC885E","full_name":"Mishra, Nikhil","orcid":"0000-0002-6425-5788"},{"id":"39427864-F248-11E8-B48F-1D18A9856A87","first_name":"Carl-Philipp J","last_name":"Heisenberg","full_name":"Heisenberg, Carl-Philipp J","orcid":"0000-0002-0912-4566"}],"isi":1,"ec_funded":1,"type":"journal_article","date_published":"2021-08-30T00:00:00Z","ddc":["570"],"publication":"Annual Review of Genetics","publication_identifier":{"issn":["0066-4197"],"eissn":["1545-2948"]},"quality_controlled":"1","article_processing_charge":"No","acknowledgement":"The authors would like to thank Feyza Nur Arslan, Suyash Naik, Diana Pinheiro, Alexandra Schauer, and Shayan Shamipour for their comments on the draft. N.M. is supported by an ISTplus postdoctoral fellowship (H2020 Marie-Sklodowska-Curie COFUND Action).","OA_place":"publisher","year":"2021","month":"08","date_updated":"2026-06-18T08:39:48Z","_id":"10406","citation":{"ista":"Mishra N, Heisenberg C-PJ. 2021. Dissecting organismal morphogenesis by bridging genetics and biophysics. Annual Review of Genetics. 55, 209–233.","short":"N. Mishra, C.-P.J. Heisenberg, Annual Review of Genetics 55 (2021) 209–233.","apa":"Mishra, N., &#38; Heisenberg, C.-P. J. (2021). Dissecting organismal morphogenesis by bridging genetics and biophysics. <i>Annual Review of Genetics</i>. Annual Reviews. <a href=\"https://doi.org/10.1146/annurev-genet-071819-103748\">https://doi.org/10.1146/annurev-genet-071819-103748</a>","ieee":"N. Mishra and C.-P. J. Heisenberg, “Dissecting organismal morphogenesis by bridging genetics and biophysics,” <i>Annual Review of Genetics</i>, vol. 55. Annual Reviews, pp. 209–233, 2021.","ama":"Mishra N, Heisenberg C-PJ. Dissecting organismal morphogenesis by bridging genetics and biophysics. <i>Annual Review of Genetics</i>. 2021;55:209-233. doi:<a href=\"https://doi.org/10.1146/annurev-genet-071819-103748\">10.1146/annurev-genet-071819-103748</a>","mla":"Mishra, Nikhil, and Carl-Philipp J. Heisenberg. “Dissecting Organismal Morphogenesis by Bridging Genetics and Biophysics.” <i>Annual Review of Genetics</i>, vol. 55, Annual Reviews, 2021, pp. 209–33, doi:<a href=\"https://doi.org/10.1146/annurev-genet-071819-103748\">10.1146/annurev-genet-071819-103748</a>.","chicago":"Mishra, Nikhil, and Carl-Philipp J Heisenberg. “Dissecting Organismal Morphogenesis by Bridging Genetics and Biophysics.” <i>Annual Review of Genetics</i>. Annual Reviews, 2021. <a href=\"https://doi.org/10.1146/annurev-genet-071819-103748\">https://doi.org/10.1146/annurev-genet-071819-103748</a>."},"page":"209-233","publisher":"Annual Reviews","corr_author":"1","language":[{"iso":"eng"}],"pmid":1,"oa_version":"Published Version","OA_type":"free access","abstract":[{"text":"Multicellular organisms develop complex shapes from much simpler, single-celled zygotes through a process commonly called morphogenesis. Morphogenesis involves an interplay between several factors, ranging from the gene regulatory networks determining cell fate and differentiation to the mechanical processes underlying cell and tissue shape changes. Thus, the study of morphogenesis has historically been based on multidisciplinary approaches at the interface of biology with physics and mathematics. Recent technological advances have further improved our ability to study morphogenesis by bridging the gap between the genetic and biophysical factors through the development of new tools for visualizing, analyzing, and perturbing these factors and their biochemical intermediaries. Here, we review how a combination of genetic, microscopic, biophysical, and biochemical approaches has aided our attempts to understand morphogenesis and discuss potential approaches that may be beneficial to such an inquiry in the future.","lang":"eng"}],"date_created":"2021-12-05T23:01:41Z","volume":55,"article_type":"original","external_id":{"pmid":["34460295"],"isi":["000747220900010"]},"scopus_import":"1","intvolume":"        55","main_file_link":[{"url":"https://doi.org/10.1146/annurev-genet-071819-103748","open_access":"1"}],"oa":1,"keyword":["morphogenesis","forward genetics","high-resolution microscopy","biophysics","biochemistry","patterning"],"status":"public","publication_status":"published","project":[{"call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships"}],"day":"30","doi":"10.1146/annurev-genet-071819-103748","department":[{"_id":"CaHe"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"acknowledgement":"This work was supported by funds from the Wyss Institute for Biologically Inspired Engineering and the Boston Biomedical Innovation Center (Pilot Award 112475; Drive Award U54HL119145). J.L., K.M.K., D.R.B., J.C.W. and P.A.S. were supported by the Harvard Medical School Department of Systems Biology. J.C.W. was further supported by the Harvard Medical School Laboratory of Systems Pharmacology. A.V., D.R.B. and P.A.S. were further supported by the Wyss Institute for Biologically Inspired Engineering. N.G.G. was sponsored by the Army Research Office under Grant Number W911NF-17-2-0092. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. We sincerely thank Amanda Graveline and the Wyss Institute at Harvard for their scientific support.","year":"2021","month":"11","citation":{"chicago":"Lee, Jungmin, Andyna Vernet, Nathalie Gruber, Kasia M. Kready, Devin R. Burrill, Jeffrey C. Way, and Pamela A. Silver. “Rational Engineering of an Erythropoietin Fusion Protein to Treat Hypoxia.” <i>Protein Engineering, Design and Selection</i>. Oxford University Press, 2021. <a href=\"https://doi.org/10.1093/protein/gzab025\">https://doi.org/10.1093/protein/gzab025</a>.","mla":"Lee, Jungmin, et al. “Rational Engineering of an Erythropoietin Fusion Protein to Treat Hypoxia.” <i>Protein Engineering, Design and Selection</i>, vol. 34, gzab025, Oxford University Press, 2021, doi:<a href=\"https://doi.org/10.1093/protein/gzab025\">10.1093/protein/gzab025</a>.","ama":"Lee J, Vernet A, Gruber N, et al. Rational engineering of an erythropoietin fusion protein to treat hypoxia. <i>Protein Engineering, Design and Selection</i>. 2021;34. doi:<a href=\"https://doi.org/10.1093/protein/gzab025\">10.1093/protein/gzab025</a>","ieee":"J. Lee <i>et al.</i>, “Rational engineering of an erythropoietin fusion protein to treat hypoxia,” <i>Protein Engineering, Design and Selection</i>, vol. 34. Oxford University Press, 2021.","short":"J. Lee, A. Vernet, N. Gruber, K.M. Kready, D.R. Burrill, J.C. Way, P.A. Silver, Protein Engineering, Design and Selection 34 (2021).","apa":"Lee, J., Vernet, A., Gruber, N., Kready, K. M., Burrill, D. R., Way, J. C., &#38; Silver, P. A. (2021). Rational engineering of an erythropoietin fusion protein to treat hypoxia. <i>Protein Engineering, Design and Selection</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/protein/gzab025\">https://doi.org/10.1093/protein/gzab025</a>","ista":"Lee J, Vernet A, Gruber N, Kready KM, Burrill DR, Way JC, Silver PA. 2021. Rational engineering of an erythropoietin fusion protein to treat hypoxia. Protein Engineering, Design and Selection. 34, gzab025."},"_id":"10363","date_updated":"2026-06-18T08:37:03Z","publisher":"Oxford University Press","language":[{"iso":"eng"}],"title":"Rational engineering of an erythropoietin fusion protein to treat hypoxia","author":[{"full_name":"Lee, Jungmin","first_name":"Jungmin","last_name":"Lee"},{"first_name":"Andyna","last_name":"Vernet","full_name":"Vernet, Andyna"},{"id":"2C9C8316-AA17-11E9-B5C2-8BC2E5697425","first_name":"Nathalie","last_name":"Gruber","full_name":"Gruber, Nathalie"},{"last_name":"Kready","first_name":"Kasia M.","full_name":"Kready, Kasia M."},{"last_name":"Burrill","first_name":"Devin R.","full_name":"Burrill, Devin R."},{"full_name":"Way, Jeffrey C.","last_name":"Way","first_name":"Jeffrey C."},{"last_name":"Silver","first_name":"Pamela A.","full_name":"Silver, Pamela A."}],"isi":1,"type":"journal_article","date_published":"2021-11-01T00:00:00Z","ddc":["570"],"publication":"Protein Engineering, Design and Selection","publication_identifier":{"issn":["1741-0126"],"eissn":["1741-0134"]},"quality_controlled":"1","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1093/protein/gzab025"}],"oa":1,"status":"public","publication_status":"published","day":"01","doi":"10.1093/protein/gzab025","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"CaGu"}],"pmid":1,"oa_version":"Published Version","article_number":"gzab025","abstract":[{"text":"Erythropoietin enhances oxygen delivery and reduces hypoxia-induced cell death, but its pro-thrombotic activity is problematic for use of erythropoietin in treating hypoxia. We constructed a fusion protein that stimulates red blood cell production and neuroprotection without triggering platelet production, a marker for thrombosis. The protein consists of an anti-glycophorin A nanobody and an erythropoietin mutant (L108A). The mutation reduces activation of erythropoietin receptor homodimers that induce erythropoiesis and thrombosis, but maintains the tissue-protective signaling. The binding of the nanobody element to glycophorin A rescues homodimeric erythropoietin receptor activation on red blood cell precursors. In a cell proliferation assay, the fusion protein is active at 10−14 M, allowing an estimate of the number of receptor–ligand complexes needed for signaling. This fusion protein stimulates erythroid cell proliferation in vitro and in mice, and shows neuroprotective activity in vitro. Our erythropoietin fusion protein presents a novel molecule for treating hypoxia.","lang":"eng"}],"date_created":"2021-11-28T23:01:28Z","volume":34,"article_type":"original","external_id":{"isi":["000746596900001"],"pmid":["34725710"]},"scopus_import":"1","intvolume":"        34"},{"language":[{"iso":"eng"}],"publisher":"Elsevier","corr_author":"1","citation":{"short":"C.-P.J. Heisenberg, A.M. Lennon, R. Mayor, G. Salbreux, Cells and Development 168 (2021).","apa":"Heisenberg, C.-P. J., Lennon, A. M., Mayor, R., &#38; Salbreux, G. (2021). Special rebranding issue: “Quantitative cell and developmental biology.” <i>Cells and Development</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.cdev.2021.203758\">https://doi.org/10.1016/j.cdev.2021.203758</a>","ista":"Heisenberg C-PJ, Lennon AM, Mayor R, Salbreux G. 2021. Special rebranding issue: “Quantitative cell and developmental biology”. Cells and Development. 168(12), 203758.","mla":"Heisenberg, Carl-Philipp J., et al. “Special Rebranding Issue: ‘Quantitative Cell and Developmental Biology.’” <i>Cells and Development</i>, vol. 168, no. 12, 203758, Elsevier, 2021, doi:<a href=\"https://doi.org/10.1016/j.cdev.2021.203758\">10.1016/j.cdev.2021.203758</a>.","ama":"Heisenberg C-PJ, Lennon AM, Mayor R, Salbreux G. Special rebranding issue: “Quantitative cell and developmental biology.” <i>Cells and Development</i>. 2021;168(12). doi:<a href=\"https://doi.org/10.1016/j.cdev.2021.203758\">10.1016/j.cdev.2021.203758</a>","ieee":"C.-P. J. Heisenberg, A. M. Lennon, R. Mayor, and G. Salbreux, “Special rebranding issue: ‘Quantitative cell and developmental biology,’” <i>Cells and Development</i>, vol. 168, no. 12. Elsevier, 2021.","chicago":"Heisenberg, Carl-Philipp J, Ana Maria Lennon, Roberto Mayor, and Guillaume Salbreux. “Special Rebranding Issue: ‘Quantitative Cell and Developmental Biology.’” <i>Cells and Development</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.cdev.2021.203758\">https://doi.org/10.1016/j.cdev.2021.203758</a>."},"_id":"10366","date_updated":"2026-06-18T08:37:21Z","month":"11","year":"2021","article_processing_charge":"No","quality_controlled":"1","publication_identifier":{"issn":["2667-2901"]},"publication":"Cells and Development","ddc":["570"],"date_published":"2021-11-17T00:00:00Z","type":"journal_article","isi":1,"author":[{"id":"39427864-F248-11E8-B48F-1D18A9856A87","first_name":"Carl-Philipp J","last_name":"Heisenberg","full_name":"Heisenberg, Carl-Philipp J","orcid":"0000-0002-0912-4566"},{"full_name":"Lennon, Ana Maria","first_name":"Ana Maria","last_name":"Lennon"},{"first_name":"Roberto","last_name":"Mayor","full_name":"Mayor, Roberto"},{"full_name":"Salbreux, Guillaume","last_name":"Salbreux","first_name":"Guillaume"}],"title":"Special rebranding issue: “Quantitative cell and developmental biology”","department":[{"_id":"CaHe"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.1016/j.cdev.2021.203758","day":"17","publication_status":"published","status":"public","main_file_link":[{"url":"https://doi.org/10.1016/j.cdev.2021.203758","open_access":"1"}],"oa":1,"issue":"12","intvolume":"       168","scopus_import":"1","external_id":{"pmid":["34800748"],"isi":["000974771600028"]},"article_type":"letter_note","date_created":"2021-11-28T23:01:30Z","volume":168,"article_number":"203758","oa_version":"Published Version","pmid":1},{"status":"public","publication_status":"published","project":[{"call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"}],"day":"09","doi":"10.15479/at:ista:10429","department":[{"_id":"GradSch"},{"_id":"DaAl"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","file_date_updated":"2022-03-28T12:55:12Z","oa":1,"degree_awarded":"PhD","supervisor":[{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian"}],"oa_version":"Published Version","related_material":{"record":[{"status":"public","id":"6673","relation":"part_of_dissertation"},{"status":"public","relation":"part_of_dissertation","id":"5965"},{"status":"public","id":"10432","relation":"part_of_dissertation"},{"status":"public","id":"10435","relation":"part_of_dissertation"}]},"abstract":[{"lang":"eng","text":"The scalability of concurrent data structures and distributed algorithms strongly depends on\r\nreducing the contention for shared resources and the costs of synchronization and communication. We show how such cost reductions can be attained by relaxing the strict consistency conditions required by sequential implementations. In the first part of the thesis, we consider relaxation in the context of concurrent data structures. Specifically, in data structures \r\nsuch as priority queues, imposing strong semantics renders scalability impossible, since a correct implementation of the remove operation should return only the element with highest priority. Intuitively, attempting to invoke remove operations concurrently  creates a race condition. This bottleneck  can be circumvented by relaxing semantics of the affected data structure, thus allowing removal of the elements which are no longer required to have the highest priority. We prove that the randomized implementations of relaxed data structures provide provable guarantees on the priority of the removed elements even under concurrency. Additionally, we show that in some cases the relaxed data structures can be used to scale the classical algorithms which are usually implemented with the exact ones. In the second part, we study parallel variants of the  stochastic gradient descent (SGD) algorithm, which distribute computation  among the multiple processors, thus reducing the running time. Unfortunately, in order for standard parallel SGD to succeed, each processor has to maintain a local copy of the necessary model parameter, which is identical to the local copies of other processors; the overheads from this perfect consistency in terms of communication and synchronization can negate the speedup gained by distributing the computation. We show that the consistency conditions required by SGD can be  relaxed, allowing the algorithm to be more flexible in terms of tolerating quantized communication, asynchrony, or even crash faults, while its convergence remains asymptotically the same."}],"date_created":"2021-12-08T21:52:28Z","month":"12","_id":"10429","date_updated":"2026-06-18T08:41:39Z","citation":{"ista":"Nadiradze G. 2021. On achieving scalability through relaxation. Institute of Science and Technology Austria.","apa":"Nadiradze, G. (2021). <i>On achieving scalability through relaxation</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:10429\">https://doi.org/10.15479/at:ista:10429</a>","short":"G. Nadiradze, On Achieving Scalability through Relaxation, Institute of Science and Technology Austria, 2021.","ieee":"G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.","ama":"Nadiradze G. On achieving scalability through relaxation. 2021. doi:<a href=\"https://doi.org/10.15479/at:ista:10429\">10.15479/at:ista:10429</a>","mla":"Nadiradze, Giorgi. <i>On Achieving Scalability through Relaxation</i>. Institute of Science and Technology Austria, 2021, doi:<a href=\"https://doi.org/10.15479/at:ista:10429\">10.15479/at:ista:10429</a>.","chicago":"Nadiradze, Giorgi. “On Achieving Scalability through Relaxation.” Institute of Science and Technology Austria, 2021. <a href=\"https://doi.org/10.15479/at:ista:10429\">https://doi.org/10.15479/at:ista:10429</a>."},"page":"132","corr_author":"1","publisher":"Institute of Science and Technology Austria","language":[{"iso":"eng"}],"file":[{"access_level":"open_access","date_updated":"2021-12-09T17:47:49Z","success":1,"content_type":"application/pdf","relation":"main_file","file_id":"10436","creator":"gnadirad","file_name":"Thesis_Final_09_12_2021.pdf","file_size":2370859,"checksum":"6bf14e9a523387328f016c0689f5e10e","date_created":"2021-12-09T17:47:49Z"},{"file_name":"Thesis_Final_09_12_2021.zip","date_created":"2021-12-09T17:47:49Z","checksum":"914d6c5ca86bd0add471971a8f4c4341","file_size":2596924,"file_id":"10437","creator":"gnadirad","content_type":"application/zip","relation":"source_file","access_level":"closed","date_updated":"2022-03-28T12:55:12Z"}],"OA_place":"publisher","year":"2021","ddc":["000"],"publication_identifier":{"issn":["2663-337X"]},"article_processing_charge":"No","alternative_title":["ISTA Thesis"],"title":"On achieving scalability through relaxation","author":[{"full_name":"Nadiradze, Giorgi","last_name":"Nadiradze","first_name":"Giorgi","id":"3279A00C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5634-0731"}],"ec_funded":1,"has_accepted_license":"1","type":"dissertation","date_published":"2021-12-09T00:00:00Z"},{"year":"2021","conference":{"end_date":"2021-12-14","start_date":"2021-12-06","location":"Sydney, Australia","name":"NeurIPS: Neural Information Processing Systems"},"acknowledgement":"We gratefully acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). PD partly conducted this work while at IST Austria and was supported by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No. 754411. SL was funded in part by European Research Council (ERC) under the European Union’s Horizon 2020 programme (grant agreement DAPP, No. 678880, and EPiGRAM-HS, No. 801039).\r\n","language":[{"iso":"eng"}],"publisher":"Neural Information Processing Systems Foundation","date_updated":"2026-06-18T08:41:40Z","_id":"10435","citation":{"apa":"Nadiradze, G., Sabour, A., Davies, P., Li, S., &#38; Alistarh, D.-A. (2021). Asynchronous decentralized SGD with quantized and local updates. In <i>35th Conference on Neural Information Processing Systems</i>. Sydney, Australia: Neural Information Processing Systems Foundation.","short":"G. Nadiradze, A. Sabour, P. Davies, S. Li, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021.","ista":"Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. 2021. Asynchronous decentralized SGD with quantized and local updates. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.","ama":"Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. Asynchronous decentralized SGD with quantized and local updates. In: <i>35th Conference on Neural Information Processing Systems</i>. Neural Information Processing Systems Foundation; 2021.","mla":"Nadiradze, Giorgi, et al. “Asynchronous Decentralized SGD with Quantized and Local Updates.” <i>35th Conference on Neural Information Processing Systems</i>, Neural Information Processing Systems Foundation, 2021.","ieee":"G. Nadiradze, A. Sabour, P. Davies, S. Li, and D.-A. Alistarh, “Asynchronous decentralized SGD with quantized and local updates,” in <i>35th Conference on Neural Information Processing Systems</i>, Sydney, Australia, 2021.","chicago":"Nadiradze, Giorgi, Amirmojtaba Sabour, Peter Davies, Shigang Li, and Dan-Adrian Alistarh. “Asynchronous Decentralized SGD with Quantized and Local Updates.” In <i>35th Conference on Neural Information Processing Systems</i>. Neural Information Processing Systems Foundation, 2021."},"month":"12","date_published":"2021-12-01T00:00:00Z","type":"conference","arxiv":1,"ec_funded":1,"author":[{"last_name":"Nadiradze","id":"3279A00C-F248-11E8-B48F-1D18A9856A87","first_name":"Giorgi","full_name":"Nadiradze, Giorgi","orcid":"0000-0001-5634-0731"},{"full_name":"Sabour, Amirmojtaba","id":"bcc145fd-e77f-11ea-ae8b-80d661dbff67","first_name":"Amirmojtaba","last_name":"Sabour"},{"orcid":"0000-0002-5646-9524","first_name":"Peter","id":"11396234-BB50-11E9-B24C-90FCE5697425","last_name":"Davies","full_name":"Davies, Peter"},{"full_name":"Li, Shigang","last_name":"Li","first_name":"Shigang"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","last_name":"Alistarh","orcid":"0000-0003-3650-940X"}],"title":"Asynchronous decentralized SGD with quantized and local updates","article_processing_charge":"No","quality_controlled":"1","publication":"35th Conference on Neural Information Processing Systems","ddc":["000"],"main_file_link":[{"open_access":"1","url":"https://papers.nips.cc/paper/2021/hash/362c99307cdc3f2d8b410652386a9dd1-Abstract.html"}],"oa":1,"department":[{"_id":"DaAl"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"01","project":[{"call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships"},{"name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223"}],"publication_status":"published","status":"public","date_created":"2021-12-09T10:59:12Z","abstract":[{"text":"Decentralized optimization is emerging as a viable alternative for scalable distributed machine learning, but also introduces new challenges in terms of synchronization costs. To this end, several communication-reduction techniques, such as non-blocking communication, quantization, and local steps, have been explored in the decentralized setting. Due to the complexity of analyzing optimization in such a relaxed setting, this line of work often assumes \\emph{global} communication rounds, which require additional synchronization. In this paper, we consider decentralized optimization in the simpler, but harder to analyze, \\emph{asynchronous gossip} model, in which communication occurs in discrete, randomly chosen pairings among nodes. Perhaps surprisingly, we show that a variant of SGD called \\emph{SwarmSGD} still converges in this setting, even if \\emph{non-blocking communication}, \\emph{quantization}, and \\emph{local steps} are all applied \\emph{in conjunction}, and even if the node data distributions and underlying graph topology are both \\emph{heterogenous}. Our analysis is based on a new connection with multi-dimensional load-balancing processes. We implement this algorithm and deploy it in a super-computing environment, showing that it can outperform previous decentralized methods in terms of end-to-end training time, and that it can even rival carefully-tuned large-batch SGD for certain tasks.","lang":"eng"}],"related_material":{"record":[{"status":"public","id":"10429","relation":"dissertation_contains"}]},"oa_version":"Published Version","external_id":{"arxiv":["1910.12308"]}},{"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"day":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"MaMo"}],"status":"public","publication_status":"published","main_file_link":[{"url":"http://proceedings.mlr.press/v139/nguyen21g.html","open_access":"1"}],"oa":1,"intvolume":"       139","external_id":{"arxiv":["2012.11654"]},"abstract":[{"text":"A recent line of work has analyzed the theoretical properties of deep neural networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue of the NTK has been related to the memorization capacity, the global convergence of gradient descent algorithms and the generalization of deep nets. However, existing results either provide bounds in the two-layer setting or assume that the spectrum of the NTK matrices is bounded away from 0 for multi-layer networks. In this paper, we provide tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU nets, both in the limiting case of infinite widths and for finite widths. In the finite-width setting, the network architectures we consider are fairly general: we require the existence of a wide layer with roughly order of $N$ neurons, $N$ being the number of data samples; and the scaling of the remaining layer widths is arbitrary (up to logarithmic factors). To obtain our results, we analyze various quantities of independent interest: we give lower bounds on the smallest singular value of hidden feature matrices, and upper bounds on the Lipschitz constant of input-output feature maps.","lang":"eng"}],"volume":139,"date_created":"2022-01-03T10:57:49Z","oa_version":"Published Version","editor":[{"first_name":"Marina","last_name":"Meila","full_name":"Meila, Marina"},{"full_name":"Zhang, Tong","first_name":"Tong","last_name":"Zhang"}],"month":"01","_id":"10595","citation":{"mla":"Nguyen, Quynh, et al. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, edited by Marina Meila and Tong Zhang, vol. 139, ML Research Press, 2021, pp. 8119–29.","ama":"Nguyen Q, Mondelli M, Montufar GF. Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In: Meila M, Zhang T, eds. <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:8119-8129.","ieee":"Q. Nguyen, M. Mondelli, and G. F. Montufar, “Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 8119–8129.","apa":"Nguyen, Q., Mondelli, M., &#38; Montufar, G. F. (2021). Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In M. Meila &#38; T. Zhang (Eds.), <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.","short":"Q. Nguyen, M. Mondelli, G.F. Montufar, in:, M. Meila, T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 8119–8129.","ista":"Nguyen Q, Mondelli M, Montufar GF. 2021. Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. Proceedings of the 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 139, 8119–8129.","chicago":"Nguyen, Quynh, Marco Mondelli, and Guido F Montufar. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, edited by Marina Meila and Tong Zhang, 139:8119–29. ML Research Press, 2021."},"date_updated":"2026-06-18T08:43:54Z","page":"8119-8129","publisher":"ML Research Press","language":[{"iso":"eng"}],"conference":{"name":"ICML: International Conference on Machine Learning","location":"Virtual","start_date":"2021-07-18","end_date":"2021-07-24"},"year":"2021","acknowledgement":"The authors would like to thank the anonymous reviewers for their helpful comments. MM was partially supported\r\nby the 2019 Lopez-Loreta Prize. QN and GM acknowledge support from the European Research Council (ERC) under\r\nthe European Union’s Horizon 2020 research and innovation programme (grant agreement no 757983).","quality_controlled":"1","article_processing_charge":"No","ddc":["000"],"publication":"Proceedings of the 38th International Conference on Machine Learning","type":"conference","date_published":"2021-01-01T00:00:00Z","title":"Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks","alternative_title":["Proceedings of Machine Learning Research"],"author":[{"first_name":"Quynh","last_name":"Nguyen","full_name":"Nguyen, Quynh"},{"full_name":"Mondelli, Marco","last_name":"Mondelli","first_name":"Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","orcid":"0000-0002-3242-7020"},{"last_name":"Montufar","first_name":"Guido F","full_name":"Montufar, Guido F"}],"arxiv":1},{"year":"2021","conference":{"end_date":"2021-02-09","location":"Virtual","start_date":"2021-02-02","name":"AAAI: Association for the Advancement of Artificial Intelligence"},"acknowledgement":"We would like to thank Christopher De Sa for his feedback on an earlier draft of this paper, as well as the anonymous AAAI reviewers for their useful comments. This project has received\r\nfunding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). Bapi\r\nChatterjee was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 754411 (ISTPlus).","language":[{"iso":"eng"}],"page":"9037-9045","_id":"10432","citation":{"chicago":"Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav  Kungurtsev, and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” In <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 35:9037–45, 2021.","short":"G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.","apa":"Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., &#38; Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In <i>Proceedings of the AAAI Conference on Artificial Intelligence</i> (Vol. 35, pp. 9037–9045). Virtual.","ista":"Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. 2021. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence vol. 35, 9037–9045.","ama":"Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In: <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Vol 35. ; 2021:9037-9045.","mla":"Nadiradze, Giorgi, et al. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 35, no. 10, 2021, pp. 9037–45.","ieee":"G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045."},"date_updated":"2026-06-18T08:41:18Z","month":"05","date_published":"2021-05-18T00:00:00Z","type":"conference","arxiv":1,"ec_funded":1,"author":[{"last_name":"Nadiradze","id":"3279A00C-F248-11E8-B48F-1D18A9856A87","first_name":"Giorgi","full_name":"Nadiradze, Giorgi","orcid":"0000-0001-5634-0731"},{"last_name":"Markov","first_name":"Ilia","id":"D0CF4148-C985-11E9-8066-0BDEE5697425","full_name":"Markov, Ilia"},{"first_name":"Bapi","id":"3C41A08A-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee","full_name":"Chatterjee, Bapi","orcid":"0000-0002-2742-4028"},{"full_name":"Kungurtsev, Vyacheslav ","last_name":"Kungurtsev","first_name":"Vyacheslav "},{"full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","orcid":"0000-0003-3650-940X"}],"title":"Elastic consistency: A practical consistency model for distributed stochastic gradient descent","article_processing_charge":"No","quality_controlled":"1","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","ddc":["000"],"main_file_link":[{"open_access":"1","url":"https://ojs.aaai.org/index.php/AAAI/article/view/17092"}],"oa":1,"issue":"10","department":[{"_id":"DaAl"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"18","project":[{"grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships"},{"grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning"}],"publication_status":"published","status":"public","date_created":"2021-12-09T09:21:35Z","volume":35,"abstract":[{"text":"One key element behind the recent progress of machine learning has been the ability to train machine learning models in large-scale distributed shared-memory and message-passing environments. Most of these models are trained employing variants of stochastic gradient descent (SGD) based optimization, but most methods involve some type of consistency relaxation relative to sequential SGD, to mitigate its large communication or synchronization costs at scale. In this paper, we introduce a general consistency condition covering communication-reduced and asynchronous distributed SGD implementations. Our framework, called elastic consistency, decouples the system-specific aspects of the implementation from the SGD convergence requirements, giving a general way to obtain convergence bounds for a wide variety of distributed SGD methods used in practice. Elastic consistency can be used to re-derive or improve several previous convergence bounds in message-passing and shared-memory settings, but also to analyze new models and distribution schemes. As a direct application, we propose and analyze a new synchronization-avoiding scheduling scheme for distributed SGD, and show that it can be used to efficiently train deep convolutional models for image classification.","lang":"eng"}],"related_material":{"record":[{"id":"10429","relation":"dissertation_contains","status":"public"}]},"oa_version":"Published Version","intvolume":"        35","external_id":{"arxiv":["2001.05918"]}},{"publisher":"Bluefors Oy","publication_status":"published","language":[{"iso":"eng"}],"month":"06","status":"public","_id":"10645","page":"8","date_updated":"2026-06-18T08:46:15Z","citation":{"chicago":"Simbierowicz, Slawomir, Chunyan Shi, Michele Collodo, Moritz Kirste, Farid Hassani, Johannes M Fink, Jonas Bylander, Daniel Perez Lozano, and Russell Lake. <i>Qubit Energy-Relaxation Statistics in the Bluefors Quantum Measurement System</i>. Helsinki, Finland: Bluefors Oy, 2021.","ieee":"S. Simbierowicz <i>et al.</i>, <i>Qubit energy-relaxation statistics in the Bluefors quantum measurement system</i>. Helsinki, Finland: Bluefors Oy, 2021.","ama":"Simbierowicz S, Shi C, Collodo M, et al. <i>Qubit Energy-Relaxation Statistics in the Bluefors Quantum Measurement System</i>. Helsinki, Finland: Bluefors Oy; 2021.","mla":"Simbierowicz, Slawomir, et al. <i>Qubit Energy-Relaxation Statistics in the Bluefors Quantum Measurement System</i>. Bluefors Oy, 2021.","ista":"Simbierowicz S, Shi C, Collodo M, Kirste M, Hassani F, Fink JM, Bylander J, Perez Lozano D, Lake R. 2021. Qubit energy-relaxation statistics in the Bluefors quantum measurement system, Helsinki, Finland: Bluefors Oy, 8p.","short":"S. Simbierowicz, C. Shi, M. Collodo, M. Kirste, F. Hassani, J.M. Fink, J. Bylander, D. Perez Lozano, R. Lake, Qubit Energy-Relaxation Statistics in the Bluefors Quantum Measurement System, Bluefors Oy, Helsinki, Finland, 2021.","apa":"Simbierowicz, S., Shi, C., Collodo, M., Kirste, M., Hassani, F., Fink, J. M., … Lake, R. (2021). <i>Qubit energy-relaxation statistics in the Bluefors quantum measurement system</i>. Helsinki, Finland: Bluefors Oy."},"department":[{"_id":"JoFi"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"03","keyword":["Application note"],"year":"2021","main_file_link":[{"open_access":"1","url":"https://bluefors.com/blog/application-note-qubit-energy-relaxation-statistics-bluefors-quantum-measurement-system/"}],"oa":1,"ddc":["530"],"article_processing_charge":"No","quality_controlled":"1","title":"Qubit energy-relaxation statistics in the Bluefors quantum measurement system","alternative_title":["Bluefors Blog"],"oa_version":"Published Version","author":[{"full_name":"Simbierowicz, Slawomir","first_name":"Slawomir","last_name":"Simbierowicz"},{"last_name":"Shi","first_name":"Chunyan","full_name":"Shi, Chunyan"},{"full_name":"Collodo, Michele","first_name":"Michele","last_name":"Collodo"},{"full_name":"Kirste, Moritz","last_name":"Kirste","first_name":"Moritz"},{"full_name":"Hassani, Farid","last_name":"Hassani","id":"2AED110C-F248-11E8-B48F-1D18A9856A87","first_name":"Farid","orcid":"0000-0001-6937-5773"},{"orcid":"0000-0001-8112-028X","full_name":"Fink, Johannes M","first_name":"Johannes M","id":"4B591CBA-F248-11E8-B48F-1D18A9856A87","last_name":"Fink"},{"full_name":"Bylander, Jonas","last_name":"Bylander","first_name":"Jonas"},{"last_name":"Perez Lozano","first_name":"Daniel","full_name":"Perez Lozano, Daniel"},{"last_name":"Lake","first_name":"Russell","full_name":"Lake, Russell"}],"date_published":"2021-06-03T00:00:00Z","date_created":"2022-01-19T08:41:14Z","type":"other_academic_publication","abstract":[{"text":"Superconducting qubits have emerged as a highly versatile and useful platform for quantum technological applications [1]. Bluefors and Zurich Instruments have supported the growth of this field from the 2010s onwards by providing well-engineered and reliable measurement infrastructure [2]– [6]. Having a long and stable qubit lifetime is a critical system property. Therefore, considerable effort has already gone into measuring qubit energy-relaxation timescales and their fluctuations, see Refs. [7]–[10] among others. Accurately extracting the statistics of a quantum device requires users to perform time consuming measurements. One measurement challenge is that the detection of the state-dependent\r\nresponse of a superconducting resonator due to a dispersively-coupled qubit requires an inherently low signal level. Consequently, measurements must be performed using a microwave probe that contains only a few microwave photons. Improving the signal-to-noise ratio (SNR) by using near-quantum limited parametric amplifiers as well as the use of optimized signal processing enabled by efficient room temperature instrumentation help to reduce measurement time. An empirical observation for fixed frequency transmons from recent literature is that as the energy-relaxation time 𝑇𝑇1 increases, so do its natural temporal fluctuations [7], [10]. This necessitates many repeated measurements to understand the statistics (see for example, Ref. [10]). In addition, as state-of-the-art qubits increase in lifetime, longer\r\nmeasurement times are expected to obtain accurate statistics. As described below, the scaling of the widths of the qubit energy-relaxation distributions also reveal clues about the origin of the energy-relaxation.","lang":"eng"}],"place":"Helsinki, Finland"},{"article_processing_charge":"No","quality_controlled":"1","ddc":["530"],"date_created":"2022-01-19T08:29:57Z","date_published":"2021-04-20T00:00:00Z","type":"other_academic_publication","place":"Helsinki, Finland","abstract":[{"text":"The purpose of this application note is to demonstrate a working example of a superconducting qubit measurement in a Bluefors cryostat using the Keysight quantum control hardware. Our motivation is twofold. First, we provide pre-qualification data that the Bluefors cryostat, including filtering and wiring, can support long-lived qubits. Second, we demonstrate that the Keysight system (controlled using Labber) provides a straightforward solution to perform these characterization measurements. This document is intended as a brief guide for starting an experimental platform for testing superconducting qubits. The setup described here is an immediate jumping off point for a suite of applications including testing quantum logical gates, quantum optics with microwaves, or even using the qubit itself as a sensitive probe of local electromagnetic fields. Qubit measurements rely on high performance of both the physical sample environment and the measurement electronics. An overview of the cryogenic system is shown in Figure 1, and an overview of the integration between the electronics and cryostat (including wiring details) is shown in Figure 2.","lang":"eng"}],"alternative_title":["Bluefors Blog"],"title":"The Bluefors dilution refrigerator as an integrated quantum measurement system","author":[{"full_name":"Lake, Russell","last_name":"Lake","first_name":"Russell"},{"full_name":"Simbierowicz, Slawomir","first_name":"Slawomir","last_name":"Simbierowicz"},{"full_name":"Krantz, Philip","last_name":"Krantz","first_name":"Philip"},{"orcid":"0000-0001-6937-5773","last_name":"Hassani","id":"2AED110C-F248-11E8-B48F-1D18A9856A87","first_name":"Farid","full_name":"Hassani, Farid"},{"orcid":"0000-0001-8112-028X","first_name":"Johannes M","id":"4B591CBA-F248-11E8-B48F-1D18A9856A87","last_name":"Fink","full_name":"Fink, Johannes M"}],"oa_version":"Published Version","department":[{"_id":"JoFi"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"20","publisher":"Bluefors Oy","publication_status":"published","language":[{"iso":"eng"}],"month":"04","citation":{"chicago":"Lake, Russell, Slawomir Simbierowicz, Philip Krantz, Farid Hassani, and Johannes M Fink. <i>The Bluefors Dilution Refrigerator as an Integrated Quantum Measurement System</i>. Helsinki, Finland: Bluefors Oy, 2021.","ista":"Lake R, Simbierowicz S, Krantz P, Hassani F, Fink JM. 2021. The Bluefors dilution refrigerator as an integrated quantum measurement system, Helsinki, Finland: Bluefors Oy, 9p.","short":"R. Lake, S. Simbierowicz, P. Krantz, F. Hassani, J.M. Fink, The Bluefors Dilution Refrigerator as an Integrated Quantum Measurement System, Bluefors Oy, Helsinki, Finland, 2021.","apa":"Lake, R., Simbierowicz, S., Krantz, P., Hassani, F., &#38; Fink, J. M. (2021). <i>The Bluefors dilution refrigerator as an integrated quantum measurement system</i>. Helsinki, Finland: Bluefors Oy.","ieee":"R. Lake, S. Simbierowicz, P. Krantz, F. Hassani, and J. M. Fink, <i>The Bluefors dilution refrigerator as an integrated quantum measurement system</i>. Helsinki, Finland: Bluefors Oy, 2021.","mla":"Lake, Russell, et al. <i>The Bluefors Dilution Refrigerator as an Integrated Quantum Measurement System</i>. Bluefors Oy, 2021.","ama":"Lake R, Simbierowicz S, Krantz P, Hassani F, Fink JM. <i>The Bluefors Dilution Refrigerator as an Integrated Quantum Measurement System</i>. Helsinki, Finland: Bluefors Oy; 2021."},"_id":"10644","date_updated":"2026-06-18T08:45:11Z","status":"public","page":"9","keyword":["Application note"],"year":"2021","oa":1,"main_file_link":[{"open_access":"1","url":"https://bluefors.com/blog/integrated-quantum-measurement-system/"}]},{"title":"Solving partially observable stochastic shortest-path games","author":[{"full_name":"Tomášek, Petr","first_name":"Petr","last_name":"Tomášek"},{"full_name":"Horák, Karel","last_name":"Horák","first_name":"Karel"},{"first_name":"Aditya","last_name":"Aradhye","full_name":"Aradhye, Aditya"},{"first_name":"Branislav","last_name":"Bošanský","full_name":"Bošanský, Branislav"},{"orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu","last_name":"Chatterjee","first_name":"Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"}],"ec_funded":1,"type":"conference","date_published":"2021-09-01T00:00:00Z","ddc":["000"],"publication":"30th International Joint Conference on Artificial Intelligence","publication_identifier":{"isbn":["9780999241196"],"issn":["1045-0823"]},"quality_controlled":"1","article_processing_charge":"No","acknowledgement":"This research was supported by the Czech Science Foundation (no. 19-24384Y), by the OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16 019/0000765 “Research Center for Informatics”, by the ERC CoG 863818 (ForM-SMArt), and by the Combat Capabilities Development Command Army Research Laboratory and was accomplished under Cooperative\r\nAgreement Number W911NF-13-2-0045 (ARL Cyber Security CRA). The views and conclusions contained in this document are those of the authors and should not be interpreted as\r\nrepresenting the official policies, either expressed or implied, of the Combat Capabilities Development Command Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes not withstanding any copyright notation here on. ","conference":{"end_date":"2021-08-27","start_date":"2021-08-19","location":"Virtual, Online","name":"IJCAI: International Joint Conferences on Artificial Intelligence"},"year":"2021","month":"09","_id":"10847","date_updated":"2026-06-18T10:41:02Z","page":"4182-4189","citation":{"apa":"Tomášek, P., Horák, K., Aradhye, A., Bošanský, B., &#38; Chatterjee, K. (2021). Solving partially observable stochastic shortest-path games. In <i>30th International Joint Conference on Artificial Intelligence</i> (pp. 4182–4189). Virtual, Online: International Joint Conferences on Artificial Intelligence. <a href=\"https://doi.org/10.24963/ijcai.2021/575\">https://doi.org/10.24963/ijcai.2021/575</a>","short":"P. Tomášek, K. Horák, A. Aradhye, B. Bošanský, K. Chatterjee, in:, 30th International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 4182–4189.","ista":"Tomášek P, Horák K, Aradhye A, Bošanský B, Chatterjee K. 2021. Solving partially observable stochastic shortest-path games. 30th International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conferences on Artificial Intelligence, 4182–4189.","mla":"Tomášek, Petr, et al. “Solving Partially Observable Stochastic Shortest-Path Games.” <i>30th International Joint Conference on Artificial Intelligence</i>, International Joint Conferences on Artificial Intelligence, 2021, pp. 4182–89, doi:<a href=\"https://doi.org/10.24963/ijcai.2021/575\">10.24963/ijcai.2021/575</a>.","ama":"Tomášek P, Horák K, Aradhye A, Bošanský B, Chatterjee K. Solving partially observable stochastic shortest-path games. In: <i>30th International Joint Conference on Artificial Intelligence</i>. International Joint Conferences on Artificial Intelligence; 2021:4182-4189. doi:<a href=\"https://doi.org/10.24963/ijcai.2021/575\">10.24963/ijcai.2021/575</a>","ieee":"P. Tomášek, K. Horák, A. Aradhye, B. Bošanský, and K. Chatterjee, “Solving partially observable stochastic shortest-path games,” in <i>30th International Joint Conference on Artificial Intelligence</i>, Virtual, Online, 2021, pp. 4182–4189.","chicago":"Tomášek, Petr, Karel Horák, Aditya Aradhye, Branislav Bošanský, and Krishnendu Chatterjee. “Solving Partially Observable Stochastic Shortest-Path Games.” In <i>30th International Joint Conference on Artificial Intelligence</i>, 4182–89. International Joint Conferences on Artificial Intelligence, 2021. <a href=\"https://doi.org/10.24963/ijcai.2021/575\">https://doi.org/10.24963/ijcai.2021/575</a>."},"publisher":"International Joint Conferences on Artificial Intelligence","language":[{"iso":"eng"}],"oa_version":"Published Version","abstract":[{"text":"We study the two-player zero-sum extension of the partially observable stochastic shortest-path problem where one agent has only partial information about the environment. We formulate this problem as a partially observable stochastic game (POSG): given a set of target states and negative rewards for each transition, the player with imperfect information maximizes the expected undiscounted total reward until a target state is reached. The second player with the perfect information aims for the opposite. We base our formalism on POSGs with one-sided observability (OS-POSGs) and give the following contributions: (1) we introduce a novel heuristic search value iteration algorithm that iteratively solves depth-limited variants of the game, (2) we derive the bound on the depth guaranteeing an arbitrary precision, (3) we propose a novel upper-bound estimation that allows early terminations, and (4) we experimentally evaluate the algorithm on a pursuit-evasion game.","lang":"eng"}],"date_created":"2022-03-13T23:01:47Z","scopus_import":"1","oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.24963/ijcai.2021/575"}],"status":"public","publication_status":"published","project":[{"_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","grant_number":"863818","call_identifier":"H2020","name":"Formal Methods for Stochastic Models: Algorithms and Applications"}],"day":"01","doi":"10.24963/ijcai.2021/575","department":[{"_id":"KrCh"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Neuronal computations depend on synaptic connectivity and intrinsic electrophysiological properties. Synaptic connectivity determines which inputs from presynaptic neurons are integrated, while cellular properties determine how inputs are filtered over time. Unlike their biological counterparts, most computational approaches to learning in simulated neural networks are limited to changes in synaptic connectivity. However, if intrinsic parameters change, neural computations are altered drastically. Here, we include the parameters that determine the intrinsic properties,\r\ne.g., time constants and reset potential, into the learning paradigm. Using sparse feedback signals that indicate target spike times, and gradient-based parameter updates, we show that the intrinsic parameters can be learned along with the synaptic weights to produce specific input-output functions. Specifically, we use a teacher-student paradigm in which a randomly initialised leaky integrate-and-fire or resonate-and-fire neuron must recover the parameters of a teacher neuron. We show that complex temporal functions can be learned online and without backpropagation through time, relying on event-based updates only. Our results are a step towards online learning of neural computations from ungraded and unsigned sparse feedback signals with a biologically inspired learning mechanism."}],"volume":20,"date_created":"2022-06-19T22:01:59Z","scopus_import":"1","intvolume":"        20","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/88e1ce84f9feef5a08d0df0334c53468-Paper.pdf"}],"oa":1,"status":"public","publication_status":"published","project":[{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z"}],"day":"01","department":[{"_id":"TiVo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Online learning of neural computations from sparse temporal feedback","author":[{"full_name":"Braun, Lukas","last_name":"Braun","first_name":"Lukas"},{"orcid":"0000-0003-3295-6181","first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","full_name":"Vogels, Tim P"}],"type":"conference","date_published":"2021-12-01T00:00:00Z","ddc":["000","570"],"publication":"Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems","publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"quality_controlled":"1","article_processing_charge":"No","acknowledgement":"We would like to thank Professor Dr. Henning Sprekeler for his valuable suggestions and Dr. Andrew Saxe, Milan Klöwer and Anna Wallis for their constructive feedback on the manuscript. Lukas Braun was supported by the Network of European Neuroscience Schools through their NENS Exchange Grant program, by the European Union through their European Community Action Scheme for the Mobility of University Students, the Woodward Scholarship awarded by Wadham College, Oxford and the Medical Research Council [MR/N013468/1]. Tim P. Vogels was supported by a Wellcome Trust Senior Research Fellowship [214316/Z/18/Z].","conference":{"end_date":"2021-12-14","location":"Virtual, Online","start_date":"2021-12-06","name":"NeurIPS: Neural Information Processing Systems"},"year":"2021","month":"12","_id":"11453","date_updated":"2026-06-18T17:17:52Z","page":"16437-16450","citation":{"mla":"Braun, Lukas, and Tim P. Vogels. “Online Learning of Neural Computations from Sparse Temporal Feedback.” <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, vol. 20, Neural Information Processing Systems Foundation, 2021, pp. 16437–50.","ama":"Braun L, Vogels TP. Online learning of neural computations from sparse temporal feedback. In: <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>. Vol 20. Neural Information Processing Systems Foundation; 2021:16437-16450.","ieee":"L. Braun and T. P. Vogels, “Online learning of neural computations from sparse temporal feedback,” in <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 20, pp. 16437–16450.","short":"L. Braun, T.P. Vogels, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 16437–16450.","apa":"Braun, L., &#38; Vogels, T. P. (2021). Online learning of neural computations from sparse temporal feedback. In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i> (Vol. 20, pp. 16437–16450). Virtual, Online: Neural Information Processing Systems Foundation.","ista":"Braun L, Vogels TP. 2021. Online learning of neural computations from sparse temporal feedback. Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 20, 16437–16450.","chicago":"Braun, Lukas, and Tim P Vogels. “Online Learning of Neural Computations from Sparse Temporal Feedback.” In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, 20:16437–50. Neural Information Processing Systems Foundation, 2021."},"corr_author":"1","publisher":"Neural Information Processing Systems Foundation","language":[{"iso":"eng"}]},{"year":"2021","conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2021-12-14","start_date":"2021-12-06","location":"Virtual, Online"},"acknowledgement":"We would like to thank the anonymous reviewers for helpful comments and suggestions. We also thank Aurelien Lucchi and Antonio Orvieto for fruitful discussions at an early stage of this work. FA is partially supported by the SNSF under research project No. 192363 and conducted part of this work while at IST Austria under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 805223 ScaleML). PD partly conducted this work while at IST Austria and was supported by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No. 754411.","page":"2823-2834","_id":"11452","citation":{"mla":"Alimisis, Foivos, et al. “Distributed Principal Component Analysis with Limited Communication.” <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, vol. 4, Neural Information Processing Systems Foundation, 2021, pp. 2823–34.","ama":"Alimisis F, Davies P, Vandereycken B, Alistarh D-A. Distributed principal component analysis with limited communication. In: <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>. Vol 4. Neural Information Processing Systems Foundation; 2021:2823-2834.","ieee":"F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 4, pp. 2823–2834.","short":"F. Alimisis, P. Davies, B. Vandereycken, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 2823–2834.","apa":"Alimisis, F., Davies, P., Vandereycken, B., &#38; Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i> (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation.","ista":"Alimisis F, Davies P, Vandereycken B, Alistarh D-A. 2021. Distributed principal component analysis with limited communication. Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 4, 2823–2834.","chicago":"Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh. “Distributed Principal Component Analysis with Limited Communication.” In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, 4:2823–34. Neural Information Processing Systems Foundation, 2021."},"date_updated":"2026-06-18T17:16:05Z","month":"12","language":[{"iso":"eng"}],"publisher":"Neural Information Processing Systems Foundation","corr_author":"1","type":"conference","date_published":"2021-12-01T00:00:00Z","author":[{"full_name":"Alimisis, Foivos","last_name":"Alimisis","first_name":"Foivos"},{"full_name":"Davies, Peter","first_name":"Peter","id":"11396234-BB50-11E9-B24C-90FCE5697425","last_name":"Davies","orcid":"0000-0002-5646-9524"},{"full_name":"Vandereycken, Bart","last_name":"Vandereycken","first_name":"Bart"},{"orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian"}],"title":"Distributed principal component analysis with limited communication","ec_funded":1,"arxiv":1,"quality_controlled":"1","publication_identifier":{"isbn":["9781713845393"],"issn":["1049-5258"]},"article_processing_charge":"No","ddc":["000"],"publication":"Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2021/file/1680e9fa7b4dd5d62ece800239bb53bd-Paper.pdf","open_access":"1"}],"oa":1,"project":[{"call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"},{"name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411"}],"day":"01","department":[{"_id":"DaAl"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","publication_status":"published","abstract":[{"lang":"eng","text":"We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly distributed among a set of computational nodes. We propose a new quantized variant of Riemannian gradient descent to solve this problem, and prove that the algorithm converges with high probability under a set of necessary spherical-convexity properties. We give bounds on the number of bits transmitted by the algorithm under common initialization schemes, and investigate the dependency on the problem dimension in each case."}],"date_created":"2022-06-19T22:01:58Z","volume":4,"oa_version":"Published Version","intvolume":"         4","external_id":{"arxiv":["2110.14391"]},"scopus_import":"1"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"GradSch"},{"_id":"DaAl"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"project":[{"grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning"}],"day":"06","publication_status":"published","status":"public","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/48000647b315f6f00f913caa757a70b3-Paper.pdf"}],"oa":1,"intvolume":"        34","scopus_import":"1","external_id":{"arxiv":["2106.12379"]},"date_created":"2022-06-20T12:11:53Z","volume":34,"abstract":[{"text":"The increasing computational requirements of deep neural networks (DNNs) have led to significant interest in obtaining DNN models that are sparse, yet accurate. Recent work has investigated the even harder case of sparse training, where the DNN weights are, for as much as possible, already sparse to reduce computational costs during training. Existing sparse training methods are often empirical and can have lower accuracy relative to the dense baseline. In this paper, we present a general approach called Alternating Compressed/DeCompressed (AC/DC) training of DNNs, demonstrate convergence for a variant of the algorithm, and show that AC/DC outperforms existing sparse training methods in accuracy at similar computational budgets; at high sparsity levels, AC/DC even outperforms existing methods that rely on accurate pre-trained dense models. An important property of AC/DC is that it allows co-training of dense and sparse models, yielding accurate sparse–dense model pairs at the end of the training process. This is useful in practice, where compressed variants may be desirable for deployment in resource-constrained settings without re-doing the entire training flow, and also provides us with insights into the accuracy gap between dense and compressed models. The code is available at: https://github.com/IST-DASLab/ACDC.","lang":"eng"}],"related_material":{"record":[{"status":"public","id":"13074","relation":"dissertation_contains"}]},"oa_version":"Published Version","language":[{"iso":"eng"}],"corr_author":"1","publisher":"Neural Information Processing Systems Foundation","_id":"11458","date_updated":"2026-06-18T17:18:20Z","page":"8557-8570","citation":{"ieee":"A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 34, pp. 8557–8570.","mla":"Krumes, Alexandra, et al. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 8557–70.","ama":"Krumes A, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. Neural Information Processing Systems Foundation; 2021:8557-8570.","ista":"Krumes A, Iofinova EB, Vladu A, Alistarh D-A. 2021. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 34, 8557–8570.","short":"A. Krumes, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 8557–8570.","apa":"Krumes, A., Iofinova, E. B., Vladu, A., &#38; Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.","chicago":"Krumes, Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:8557–70. Neural Information Processing Systems Foundation, 2021."},"month":"12","year":"2021","conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2021-12-06","location":"Virtual, Online","end_date":"2021-12-14"},"acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), and a CNRS PEPS grant. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). We would also like to thank Christoph Lampert for his feedback on an earlier version of this work, as well as for providing hardware for the Transformer-XL experiments.","article_processing_charge":"No","quality_controlled":"1","publication_identifier":{"isbn":["9781713845393"],"issn":["1049-5258"]},"publication":"35th Conference on Neural Information Processing Systems","ddc":["000"],"date_published":"2021-12-06T00:00:00Z","type":"conference","arxiv":1,"ec_funded":1,"author":[{"last_name":"Peste","first_name":"Elena-Alexandra","id":"32D78294-F248-11E8-B48F-1D18A9856A87","full_name":"Peste, Elena-Alexandra"},{"orcid":"0000-0002-7778-3221","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","first_name":"Eugenia B","last_name":"Iofinova","full_name":"Iofinova, Eugenia B"},{"last_name":"Vladu","first_name":"Adrian","full_name":"Vladu, Adrian"},{"first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X"}],"alternative_title":["Advances in Neural Information Processing Systems"],"title":"AC/DC: Alternating Compressed/DeCompressed training of deep neural networks"},{"acknowledgement":"We gratefully acknowledge funding the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), as well as computational support from Amazon Web Services (AWS) EC2.","conference":{"location":"Virtual, Online","start_date":"2021-12-06","end_date":"2021-12-14","name":"NeurIPS: Neural Information Processing Systems"},"year":"2021","month":"12","_id":"11463","date_updated":"2026-06-18T17:18:44Z","citation":{"chicago":"Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:14873–86. Neural Information Processing Systems Foundation, 2021.","apa":"Frantar, E., Kurtic, E., &#38; Alistarh, D.-A. (2021). M-FAC: Efficient matrix-free approximations of second-order information. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 14873–14886). Virtual, Online: Neural Information Processing Systems Foundation.","short":"E. Frantar, E. Kurtic, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 14873–14886.","ista":"Frantar E, Kurtic E, Alistarh D-A. 2021. M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 34, 14873–14886.","mla":"Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 14873–86.","ama":"Frantar E, Kurtic E, Alistarh D-A. M-FAC: Efficient matrix-free approximations of second-order information. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. Neural Information Processing Systems Foundation; 2021:14873-14886.","ieee":"E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 34, pp. 14873–14886."},"page":"14873-14886","corr_author":"1","publisher":"Neural Information Processing Systems Foundation","language":[{"iso":"eng"}],"alternative_title":["Advances in Neural Information Processing Systems"],"title":"M-FAC: Efficient matrix-free approximations of second-order information","author":[{"full_name":"Frantar, Elias","first_name":"Elias","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","last_name":"Frantar"},{"full_name":"Kurtic, Eldar","last_name":"Kurtic","id":"47beb3a5-07b5-11eb-9b87-b108ec578218","first_name":"Eldar"},{"last_name":"Alistarh","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X"}],"ec_funded":1,"arxiv":1,"type":"conference","date_published":"2021-12-06T00:00:00Z","ddc":["000"],"publication":"35th Conference on Neural Information Processing Systems","publication_identifier":{"isbn":["9781713845393"],"issn":["1049-5258"]},"quality_controlled":"1","article_processing_charge":"No","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2021/file/7cfd5df443b4eb0d69886a583b33de4c-Paper.pdf","open_access":"1"}],"oa":1,"status":"public","publication_status":"published","project":[{"name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223"}],"day":"06","department":[{"_id":"DaAl"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","abstract":[{"text":"Efficiently approximating local curvature information of the loss function is a key tool for optimization and compression of deep neural networks. Yet, most existing methods to approximate second-order information have high computational\r\nor storage costs, which limits their practicality. In this work, we investigate matrix-free, linear-time approaches for estimating Inverse-Hessian Vector Products (IHVPs) for the case when the Hessian can be approximated as a sum of rank-one matrices, as in the classic approximation of the Hessian by the empirical Fisher matrix. We propose two new algorithms: the first is tailored towards network compression and can compute the IHVP for dimension d, if the Hessian is given as a sum of m rank-one matrices, using O(dm2) precomputation, O(dm) cost for computing the IHVP, and query cost O(m) for any single element of the inverse Hessian. The second algorithm targets an optimization setting, where we wish to compute the product between the inverse Hessian, estimated over a sliding window of optimization steps, and a given gradient direction, as required for preconditioned SGD. We give an algorithm with cost O(dm + m2) for computing the IHVP and O(dm + m3) for adding or removing any gradient from the sliding window. These\r\ntwo algorithms yield state-of-the-art results for network pruning and optimization with lower computational overhead relative to existing second-order methods. Implementations are available at [9] and [17].","lang":"eng"}],"volume":34,"date_created":"2022-06-26T22:01:35Z","external_id":{"arxiv":["2010.08222"]},"scopus_import":"1","intvolume":"        34"},{"ddc":["000"],"publication":"35th Conference on Neural Information Processing Systems","publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"quality_controlled":"1","article_processing_charge":"No","alternative_title":["Advances in Neural Information Processing Systems"],"title":"Towards tight communication lower bounds for distributed optimisation","author":[{"orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian"},{"last_name":"Korhonen","first_name":"Janne","id":"C5402D42-15BC-11E9-A202-CA2BE6697425","full_name":"Korhonen, Janne"}],"arxiv":1,"ec_funded":1,"type":"conference","date_published":"2021-12-06T00:00:00Z","month":"12","_id":"11464","citation":{"apa":"Alistarh, D.-A., &#38; Korhonen, J. (2021). Towards tight communication lower bounds for distributed optimisation. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 7254–7266). Virtual, Online: Neural Information Processing Systems Foundation.","short":"D.-A. Alistarh, J. Korhonen, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 7254–7266.","ista":"Alistarh D-A, Korhonen J. 2021. Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 34, 7254–7266.","mla":"Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 7254–66.","ama":"Alistarh D-A, Korhonen J. Towards tight communication lower bounds for distributed optimisation. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. Neural Information Processing Systems Foundation; 2021:7254-7266.","ieee":"D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 34, pp. 7254–7266.","chicago":"Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:7254–66. Neural Information Processing Systems Foundation, 2021."},"date_updated":"2026-06-18T17:19:18Z","page":"7254-7266","publisher":"Neural Information Processing Systems Foundation","corr_author":"1","language":[{"iso":"eng"}],"acknowledgement":"We thank the NeurIPS reviewers for insightful comments that helped us improve the positioning of our results, as well as for pointing out the subsampling approach for complementing the randomised lower bound. We also thank Foivos Alimisis and Peter Davies for useful discussions. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML).","conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2021-12-06","location":"Virtual, Online","end_date":"2021-12-14"},"year":"2021","external_id":{"arxiv":["2010.08222"]},"scopus_import":"1","intvolume":"        34","oa_version":"Published Version","abstract":[{"text":"We consider a standard distributed optimisation setting where N machines, each holding a d-dimensional function\r\nfi, aim to jointly minimise the sum of the functions ∑Ni=1fi(x). This problem arises naturally in large-scale distributed optimisation, where a standard solution is to apply variants of (stochastic) gradient descent. We focus on the communication complexity of this problem: our main result provides the first fully unconditional bounds on total number of bits which need to be sent and received by the N machines to solve this problem under point-to-point communication, within a given error-tolerance. Specifically, we show that Ω(Ndlogd/Nε) total bits need to be communicated between the machines to find an additive ϵ-approximation to the minimum of ∑Ni=1fi(x). The result holds for both deterministic and randomised algorithms, and, importantly, requires no assumptions on the algorithm structure. The lower bound is tight under certain restrictions on parameter values, and is matched within constant factors for quadratic objectives by a new variant of quantised gradient descent, which we describe and analyse. Our results bring over tools from communication complexity to distributed optimisation, which has potential for further applications.","lang":"eng"}],"volume":34,"date_created":"2022-06-26T22:01:35Z","status":"public","publication_status":"published","project":[{"call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"}],"day":"06","department":[{"_id":"DaAl"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/3b92d18aa7a6176dd37d372bc2f1eb71-Paper.pdf"}]},{"type":"journal_article","date_published":"2021-03-15T00:00:00Z","author":[{"full_name":"Lenz, Daniel","first_name":"Daniel","last_name":"Lenz"},{"last_name":"Schmidt","first_name":"Marcel","full_name":"Schmidt, Marcel"},{"orcid":"0000-0002-0519-4241","first_name":"Melchior","id":"88644358-0A0E-11EA-8FA5-49A33DDC885E","last_name":"Wirth","full_name":"Wirth, Melchior"}],"title":"Uniqueness of form extensions and domination of semigroups","quality_controlled":"1","publication_identifier":{"issn":["0022-1236"],"eissn":["1096-0783"]},"article_processing_charge":"No","ddc":["500"],"publication":"Journal of Functional Analysis","year":"2021","OA_place":"publisher","citation":{"short":"D. Lenz, M. Schmidt, M. Wirth, Journal of Functional Analysis 280 (2021).","apa":"Lenz, D., Schmidt, M., &#38; Wirth, M. (2021). Uniqueness of form extensions and domination of semigroups. <i>Journal of Functional Analysis</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jfa.2020.108848\">https://doi.org/10.1016/j.jfa.2020.108848</a>","ista":"Lenz D, Schmidt M, Wirth M. 2021. Uniqueness of form extensions and domination of semigroups. Journal of Functional Analysis. 280(6), 108848.","ama":"Lenz D, Schmidt M, Wirth M. Uniqueness of form extensions and domination of semigroups. <i>Journal of Functional Analysis</i>. 2021;280(6). doi:<a href=\"https://doi.org/10.1016/j.jfa.2020.108848\">10.1016/j.jfa.2020.108848</a>","mla":"Lenz, Daniel, et al. “Uniqueness of Form Extensions and Domination of Semigroups.” <i>Journal of Functional Analysis</i>, vol. 280, no. 6, 108848, Elsevier, 2021, doi:<a href=\"https://doi.org/10.1016/j.jfa.2020.108848\">10.1016/j.jfa.2020.108848</a>.","ieee":"D. Lenz, M. Schmidt, and M. Wirth, “Uniqueness of form extensions and domination of semigroups,” <i>Journal of Functional Analysis</i>, vol. 280, no. 6. Elsevier, 2021.","chicago":"Lenz, Daniel, Marcel Schmidt, and Melchior Wirth. “Uniqueness of Form Extensions and Domination of Semigroups.” <i>Journal of Functional Analysis</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.jfa.2020.108848\">https://doi.org/10.1016/j.jfa.2020.108848</a>."},"_id":"15261","date_updated":"2026-06-18T17:46:54Z","month":"03","language":[{"iso":"eng"}],"corr_author":"1","publisher":"Elsevier","abstract":[{"text":"In this article, we study uniqueness of form extensions in a rather general setting. The method is based on the theory of ordered Hilbert spaces and the concept of domination of semigroups. Our main abstract result transfers uniqueness of form extension of a dominating form to that of a dominated form. This result can be applied to a multitude of examples including various magnetic Schrödinger forms on graphs and on manifolds.","lang":"eng"}],"article_number":"108848","article_type":"original","volume":280,"date_created":"2024-04-03T07:24:57Z","oa_version":"Published Version","OA_type":"free access","intvolume":"       280","scopus_import":"1","oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.jfa.2020.108848"}],"keyword":["Analysis"],"issue":"6","day":"15","department":[{"_id":"JaMa"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.1016/j.jfa.2020.108848","status":"public","publication_status":"published"},{"citation":{"ista":"Alimisis F, Orvieto A, Becigneul G, Lucchi A. 2021. Momentum improves optimization on Riemannian manifolds. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 130, 1351–1359.","apa":"Alimisis, F., Orvieto, A., Becigneul, G., &#38; Lucchi, A. (2021). Momentum improves optimization on Riemannian manifolds. In <i>Proceedings of the 24th International Conference on Artificial Intelligence and Statistics</i> (Vol. 130, pp. 1351–1359). San Diego, CA, United States; Virtual: ML Research Press.","short":"F. Alimisis, A. Orvieto, G. Becigneul, A. Lucchi, in:, Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2021, pp. 1351–1359.","ieee":"F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in <i>Proceedings of the 24th International Conference on Artificial Intelligence and Statistics</i>, San Diego, CA, United States; Virtual, 2021, vol. 130, pp. 1351–1359.","mla":"Alimisis, Foivos, et al. “Momentum Improves Optimization on Riemannian Manifolds.” <i>Proceedings of the 24th International Conference on Artificial Intelligence and Statistics</i>, vol. 130, ML Research Press, 2021, pp. 1351–59.","ama":"Alimisis F, Orvieto A, Becigneul G, Lucchi A. Momentum improves optimization on Riemannian manifolds. In: <i>Proceedings of the 24th International Conference on Artificial Intelligence and Statistics</i>. Vol 130. ML Research Press; 2021:1351-1359.","chicago":"Alimisis, Foivos, Antonio Orvieto, Gary Becigneul, and Aurelien Lucchi. “Momentum Improves Optimization on Riemannian Manifolds.” In <i>Proceedings of the 24th International Conference on Artificial Intelligence and Statistics</i>, 130:1351–59. ML Research Press, 2021."},"_id":"15263","date_updated":"2026-06-18T17:47:19Z","page":"1351-1359","month":"04","language":[{"iso":"eng"}],"publisher":"ML Research Press","acknowledgement":"The authors would like to thank professors Nicolas Boumal and Suvrit Sra for helpful discussions on the content of this paper. Gary Bécigneul was funded by the Max Planck ETH Center for Learning Systems during the course of this work.","year":"2021","conference":{"end_date":"2021-04-15","location":"San Diego, CA, United States; Virtual","start_date":"2021-04-13","name":"AISTATS: Conference on Artificial Intelligence and Statistics"},"ddc":["000"],"publication":"Proceedings of the 24th International Conference on Artificial Intelligence and Statistics","quality_controlled":"1","article_processing_charge":"No","author":[{"last_name":"Alimisis","first_name":"Foivos","id":"19430a34-05f6-11ef-890d-c079cfc60ae2","full_name":"Alimisis, Foivos"},{"last_name":"Orvieto","first_name":"Antonio","full_name":"Orvieto, Antonio"},{"last_name":"Becigneul","first_name":"Gary","full_name":"Becigneul, Gary"},{"full_name":"Lucchi, Aurelien","last_name":"Lucchi","first_name":"Aurelien"}],"title":"Momentum improves optimization on Riemannian manifolds","alternative_title":["PMLR"],"arxiv":1,"type":"conference","date_published":"2021-04-15T00:00:00Z","status":"public","publication_status":"published","day":"15","department":[{"_id":"DaAl"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"main_file_link":[{"open_access":"1","url":"https://proceedings.mlr.press/v130/alimisis21a.html"}],"external_id":{"arxiv":["2002.04144"]},"intvolume":"       130","oa_version":"Published Version","abstract":[{"text":"We develop a new Riemannian descent algorithm that relies on momentum to improve over existing first-order methods for geodesically convex optimization. In contrast, accelerated convergence rates proved in prior work have only been shown to hold for geodesically strongly-convex objective functions. We further extend our algorithm to geodesically weakly-quasi-convex objectives. Our proofs of convergence rely on a novel estimate sequence that illustrates the dependency of the convergence rate on the curvature of the manifold. We validate our theoretical results empirically on several optimization problems defined on the sphere and on the manifold of positive definite matrices.","lang":"eng"}],"volume":130,"date_created":"2024-04-03T07:29:49Z"}]