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The upper bounds are given in terms of the ground state energy band shifted by momentum-independent excitation energies determined by an effective Hamiltonian of Bogoliubov type."}],"year":"2023","keyword":["General Medicine"],"scopus_import":"1","OA_type":"green","_id":"14854","day":"15","article_processing_charge":"No","date_published":"2023-12-15T00:00:00Z","corr_author":"1","publication_status":"published","citation":{"apa":"Mitrouskas, D. J., &#38; Seiringer, R. (2023). Ubiquity of bound states for the strongly coupled polaron. <i>Pure and Applied Analysis</i>. Mathematical Sciences Publishers. <a href=\"https://doi.org/10.2140/paa.2023.5.973\">https://doi.org/10.2140/paa.2023.5.973</a>","short":"D.J. Mitrouskas, R. Seiringer, Pure and Applied Analysis 5 (2023) 973–1008.","ama":"Mitrouskas DJ, Seiringer R. Ubiquity of bound states for the strongly coupled polaron. <i>Pure and Applied Analysis</i>. 2023;5(4):973-1008. doi:<a href=\"https://doi.org/10.2140/paa.2023.5.973\">10.2140/paa.2023.5.973</a>","mla":"Mitrouskas, David Johannes, and Robert Seiringer. “Ubiquity of Bound States for the Strongly Coupled Polaron.” <i>Pure and Applied Analysis</i>, vol. 5, no. 4, Mathematical Sciences Publishers, 2023, pp. 973–1008, doi:<a href=\"https://doi.org/10.2140/paa.2023.5.973\">10.2140/paa.2023.5.973</a>.","ista":"Mitrouskas DJ, Seiringer R. 2023. Ubiquity of bound states for the strongly coupled polaron. Pure and Applied Analysis. 5(4), 973–1008.","chicago":"Mitrouskas, David Johannes, and Robert Seiringer. “Ubiquity of Bound States for the Strongly Coupled Polaron.” <i>Pure and Applied Analysis</i>. Mathematical Sciences Publishers, 2023. <a href=\"https://doi.org/10.2140/paa.2023.5.973\">https://doi.org/10.2140/paa.2023.5.973</a>.","ieee":"D. J. Mitrouskas and R. 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Vienna, Austria &#38; Virtual: European Geosciences Union. <a href=\"https://doi.org/10.5194/egusphere-egu23-15870\">https://doi.org/10.5194/egusphere-egu23-15870</a>"},"file":[{"file_name":"2023_EGU_Abramian.pdf","file_size":294690,"file_id":"14935","date_updated":"2024-02-05T08:10:43Z","success":1,"creator":"dernst","date_created":"2024-02-05T08:10:43Z","access_level":"open_access","checksum":"8cd5bb8bf3d48ceefb395b6875819147","relation":"main_file","content_type":"application/pdf"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","has_accepted_license":"1","date_created":"2024-01-22T12:10:52Z","doi":"10.5194/egusphere-egu23-15870","publisher":"European Geosciences Union","author":[{"last_name":"Abramian","full_name":"Abramian, Sophie","first_name":"Sophie"},{"full_name":"Muller, Caroline J","last_name":"Muller","orcid":"0000-0001-5836-5350","first_name":"Caroline J","id":"f978ccb0-3f7f-11eb-b193-b0e2bd13182b"},{"full_name":"Risi, Camille","last_name":"Risi","first_name":"Camille"}],"oa_version":"Published Version"},{"oa_version":"Published Version","publication_identifier":{"eissn":["2788-3116"]},"file_date_updated":"2024-01-24T09:34:43Z","language":[{"iso":"eng"}],"has_accepted_license":"1","isi":1,"month":"09","publication":"Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications","type":"conference","external_id":{"arxiv":["2209.09860"],"isi":["001448447300005"]},"title":"Constructing Hamilton cycles and perfect matchings efficiently","conference":{"end_date":"2023-09-01","location":"Prague, Czech Republic","start_date":"2023-08-28","name":"EUROCOMB: European Conference on Combinatorics, Graph Theory and Applications"},"ddc":["510"],"arxiv":1,"ec_funded":1,"date_updated":"2025-09-09T14:24:21Z","project":[{"name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","grant_number":"101034413","call_identifier":"H2020"}],"department":[{"_id":"MaKw"}],"quality_controlled":"1","author":[{"first_name":"Michael","id":"0b2a4358-bb35-11ec-b7b9-e3279b593dbb","full_name":"Anastos, Michael","last_name":"Anastos"}],"publisher":"Masaryk University Press","date_created":"2024-01-22T12:20:15Z","doi":"10.5817/cz.muni.eurocomb23-005","corr_author":"1","date_published":"2023-09-01T00:00:00Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","file":[{"date_updated":"2024-01-24T09:34:43Z","file_id":"14881","file_name":"2023_Eurocomb_Anastos.pdf","file_size":464230,"success":1,"creator":"dernst","date_created":"2024-01-24T09:34:43Z","access_level":"open_access","relation":"main_file","checksum":"fb1d9a1e7389d90ec0e5e76934373cf8","content_type":"application/pdf"}],"publication_status":"published","citation":{"ieee":"M. Anastos, “Constructing Hamilton cycles and perfect matchings efficiently,” in <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>, Prague, Czech Republic, 2023, pp. 36–41.","chicago":"Anastos, Michael. “Constructing Hamilton Cycles and Perfect Matchings Efficiently.” In <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>, 36–41. Masaryk University Press, 2023. <a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">https://doi.org/10.5817/cz.muni.eurocomb23-005</a>.","mla":"Anastos, Michael. “Constructing Hamilton Cycles and Perfect Matchings Efficiently.” <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>, Masaryk University Press, 2023, pp. 36–41, doi:<a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">10.5817/cz.muni.eurocomb23-005</a>.","ista":"Anastos M. 2023. Constructing Hamilton cycles and perfect matchings efficiently. Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications. EUROCOMB: European Conference on Combinatorics, Graph Theory and Applications, 36–41.","ama":"Anastos M. Constructing Hamilton cycles and perfect matchings efficiently. In: <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>. Masaryk University Press; 2023:36-41. doi:<a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">10.5817/cz.muni.eurocomb23-005</a>","short":"M. Anastos, in:, Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications, Masaryk University Press, 2023, pp. 36–41.","apa":"Anastos, M. (2023). Constructing Hamilton cycles and perfect matchings efficiently. In <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i> (pp. 36–41). Prague, Czech Republic: Masaryk University Press. <a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">https://doi.org/10.5817/cz.muni.eurocomb23-005</a>"},"status":"public","oa":1,"day":"01","article_processing_charge":"No","abstract":[{"text":"<jats:p>Starting with the empty graph on $[n]$, at each round, a set of $K=K(n)$ edges is presented chosen uniformly at random from the ones that have not been presented yet. We are then asked to choose at most one of the presented edges and add it to the current graph. Our goal is to construct a Hamiltonian graph with $(1+o(1))n$ edges within as few rounds as possible. We show that in this process, one can build a Hamiltonian graph of size $(1+o(1))n$ in $(1+o(1))(1+(\\log n)/2K) n$ rounds w.h.p. The case $K=1$ implies that w.h.p. one can build a Hamiltonian graph by choosing $(1+o(1))n$ edges in an online fashion as they appear along the first $(0.5+o(1))n\\log n$ rounds of the random graph process. This answers a question of Frieze, Krivelevich and Michaeli. Observe that the number of rounds is asymptotically optimal as the first $0.5n\\log n$ edges do not span a Hamilton cycle w.h.p. The case $K=\\Theta(\\log n)$ implies that the Hamiltonicity threshold of the corresponding Achlioptas process is at most $(1+o(1))(1+(\\log n)/2K) n$. This matches the $(1-o(1))(1+(\\log n)/2K) n$ lower bound due to Krivelevich, Lubetzky and Sudakov and resolves the problem of determining the Hamiltonicity threshold of the Achlioptas process with $K=\\Theta(\\log n)$. We also show that in the above process one can construct a graph $G$ that spans a matching of size $\\lfloor V(G)/2) \\rfloor$ and $(0.5+o(1))n$ edges within $(1+o(1))(0.5+(\\log n)/2K) n$ rounds w.h.p. Our proof relies on a robust Hamiltonicity property of the strong $4$-core of the binomial random graph which we use as a black-box. This property allows it to absorb paths covering vertices outside the strong $4$-core into a cycle.</jats:p>","lang":"eng"}],"tmp":{"image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"year":"2023","page":"36-41","_id":"14867","acknowledgement":"This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034413.\r\n"},{"author":[{"first_name":"Swati","last_name":"Tyagi","full_name":"Tyagi, Swati"},{"first_name":"Juliana S.","last_name":"Capitanio","full_name":"Capitanio, Juliana S."},{"first_name":"Jiawei","last_name":"Xu","full_name":"Xu, Jiawei"},{"full_name":"Chen, Fei","last_name":"Chen","first_name":"Fei"},{"first_name":"Rahul","last_name":"Sharma","full_name":"Sharma, Rahul"},{"first_name":"Jialiang","last_name":"Huang","full_name":"Huang, Jialiang"},{"first_name":"Martin W","id":"86c0d31b-b4eb-11ec-ac5a-eae7b2e135ed","last_name":"HETZER","orcid":"0000-0002-2111-992X","full_name":"HETZER, Martin W"}],"oa_version":"Submitted Version","doi":"10.7554/elife.87462","date_created":"2024-01-22T12:21:56Z","publisher":"eLife Sciences Publications","corr_author":"1","date_published":"2023-06-23T00:00:00Z","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://doi.org/10.7554/eLife.87462.1","open_access":"1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"month":"06","publication":"eLife","status":"public","publication_status":"epub_ahead","citation":{"ama":"Tyagi S, Capitanio JS, Xu J, et al. High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope. <i>eLife</i>. 2023. doi:<a href=\"https://doi.org/10.7554/elife.87462\">10.7554/elife.87462</a>","short":"S. Tyagi, J.S. Capitanio, J. Xu, F. Chen, R. Sharma, J. Huang, M. Hetzer, ELife (2023).","apa":"Tyagi, S., Capitanio, J. S., Xu, J., Chen, F., Sharma, R., Huang, J., &#38; Hetzer, M. (2023). High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/elife.87462\">https://doi.org/10.7554/elife.87462</a>","chicago":"Tyagi, Swati, Juliana S. Capitanio, Jiawei Xu, Fei Chen, Rahul Sharma, Jialiang Huang, and Martin Hetzer. “High-Precision Mapping of Nuclear Pore-Chromatin Interactions Reveals New Principles of Genome Organization at the Nuclear Envelope.” <i>ELife</i>. eLife Sciences Publications, 2023. <a href=\"https://doi.org/10.7554/elife.87462\">https://doi.org/10.7554/elife.87462</a>.","ieee":"S. Tyagi <i>et al.</i>, “High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope,” <i>eLife</i>. eLife Sciences Publications, 2023.","mla":"Tyagi, Swati, et al. “High-Precision Mapping of Nuclear Pore-Chromatin Interactions Reveals New Principles of Genome Organization at the Nuclear Envelope.” <i>ELife</i>, eLife Sciences Publications, 2023, doi:<a href=\"https://doi.org/10.7554/elife.87462\">10.7554/elife.87462</a>.","ista":"Tyagi S, Capitanio JS, Xu J, Chen F, Sharma R, Huang J, Hetzer M. 2023. High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope. eLife."},"type":"journal_article","title":"High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope","article_processing_charge":"Yes","day":"23","article_type":"original","year":"2023","abstract":[{"lang":"eng","text":"The role of nuclear pore complexes (NPCs) in genome organization remains poorly characterized due to technical limitations in probing genome-wide protein-DNA interactions specific to the nuclear periphery. Here, we developed a new sensitive method, NPC-DamID, which combines in vitro reconstitution of nuclear import and DamID technology. The fixation-free method identifies chromatin interactions at the NPCs in intact nuclei from cells and tissues. We found that NPCs are preferentially associated with common and hierarchically arranged super-enhancers (SEs) across multiple cell types. We also uncovered phase-separated condensates at NPCs that compartmentalize and concentrate transcriptional coactivators and structural proteins at SE-regulated genes. Our results support NPCs as anchoring sites for SE regulatory hubs and cell-type-specific transcriptional control."}],"date_updated":"2024-07-31T11:56:25Z","_id":"14868","department":[{"_id":"MaHe"}],"acknowledgement":"This work was supported by M.H.’s NIH R01 grants (NS096786, GM126829) and Salk Cancer Center Support Grant P30 CA014195. M.H. also received financial support from the W.M. Keck Foundation and the NOMIS Foundation. Further, M.H. received support from the AHA-Allen Initiative in Brain Health and Cognitive Impairment award made jointly through the American Heart Association and The Paul G. Allen Frontiers Group (19PABH134610000).\r\n\r\nS.T. and J.C. were supported by Salk’s Women & Science Awards. S.T. also received financial support from the Hewitt Foundation fellowship, and J.C. is a Paul F. Glenn Biology of Aging fellow. J.H. was supported by the National Natural Science Foundation of China (31871317 and 32070635).\r\n\r\nWe thank Roberta Schulte for assistance with in vitro transport assays, for comments that greatly improved the manuscript, and for helping refine the figures presented in this work. We thank Shefali Krishna for creating the diagram for the NPC-DamID method, for her input on super-resolution microscopy analysis, and her insightful comments on this manuscript. We thank all members of the Hetzer lab for helpful discussions of these research ideas and their thoughtful comments on this manuscript. We are also grateful to Salk’s core facilities for their assistance. Specifically, we thank the Next Generation Sequencing Core (NGS) for sequencing our DamID and RNA NGS libraries, the Advanced Biophotonics Core for assistance with super-resolution microscopy, and the Razavi Newman Integrative Genomics and Bioinformatics Core (IGC) for their input on analysis methods for DamID experiments."},{"publication_identifier":{"isbn":["9781957171296"]},"oa_version":"None","author":[{"last_name":"Sahu","orcid":"0000-0001-6264-2162","full_name":"Sahu, Rishabh","first_name":"Rishabh","id":"47D26E34-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Liu","last_name":"Qiu","full_name":"Qiu, Liu"},{"first_name":"William J","id":"29705398-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9868-2166","last_name":"Hease","full_name":"Hease, William J"},{"full_name":"Arnold, Georg M","orcid":"0000-0003-1397-7876","last_name":"Arnold","id":"3770C838-F248-11E8-B48F-1D18A9856A87","first_name":"Georg M"},{"last_name":"Minoguchi","full_name":"Minoguchi, Yuri","first_name":"Yuri"},{"first_name":"Peter","last_name":"Rabl","full_name":"Rabl, Peter"},{"id":"4B591CBA-F248-11E8-B48F-1D18A9856A87","first_name":"Johannes M","orcid":"0000-0001-8112-028X","last_name":"Fink","full_name":"Fink, Johannes M"}],"publisher":"Optica Publishing Group","doi":"10.1364/ls.2023.lm1f.3","date_created":"2024-01-22T12:29:41Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"R. Sahu <i>et al.</i>, “Entangling microwaves and telecom wavelength light,” in <i>Frontiers in Optics + Laser Science 2023</i>, Tacoma, WA, United States, 2023.","chicago":"Sahu, Rishabh, Liu Qiu, William J Hease, Georg M Arnold, Yuri Minoguchi, Peter Rabl, and Johannes M Fink. “Entangling Microwaves and Telecom Wavelength Light.” In <i>Frontiers in Optics + Laser Science 2023</i>. Optica Publishing Group, 2023. <a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">https://doi.org/10.1364/ls.2023.lm1f.3</a>.","mla":"Sahu, Rishabh, et al. “Entangling Microwaves and Telecom Wavelength Light.” <i>Frontiers in Optics + Laser Science 2023</i>, LM1F.3, Optica Publishing Group, 2023, doi:<a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">10.1364/ls.2023.lm1f.3</a>.","ista":"Sahu R, Qiu L, Hease WJ, Arnold GM, Minoguchi Y, Rabl P, Fink JM. 2023. Entangling microwaves and telecom wavelength light. Frontiers in Optics + Laser Science 2023. Laser Science, LM1F.3.","ama":"Sahu R, Qiu L, Hease WJ, et al. Entangling microwaves and telecom wavelength light. In: <i>Frontiers in Optics + Laser Science 2023</i>. Optica Publishing Group; 2023. doi:<a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">10.1364/ls.2023.lm1f.3</a>","short":"R. Sahu, L. Qiu, W.J. Hease, G.M. Arnold, Y. Minoguchi, P. Rabl, J.M. Fink, in:, Frontiers in Optics + Laser Science 2023, Optica Publishing Group, 2023.","apa":"Sahu, R., Qiu, L., Hease, W. J., Arnold, G. M., Minoguchi, Y., Rabl, P., &#38; Fink, J. M. (2023). Entangling microwaves and telecom wavelength light. In <i>Frontiers in Optics + Laser Science 2023</i>. Tacoma, WA, United States: Optica Publishing Group. <a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">https://doi.org/10.1364/ls.2023.lm1f.3</a>"},"publication_status":"published","publication":"Frontiers in Optics + Laser Science 2023","status":"public","month":"10","date_published":"2023-10-01T00:00:00Z","language":[{"iso":"eng"}],"corr_author":"1","title":"Entangling microwaves and telecom wavelength light","conference":{"name":"Laser Science","location":"Tacoma, WA, United States","start_date":"2023-10-09","end_date":"2023-10-12"},"article_number":"LM1F.3","article_processing_charge":"No","day":"01","type":"conference","_id":"14872","department":[{"_id":"JoFi"}],"quality_controlled":"1","date_updated":"2024-10-09T21:07:59Z","abstract":[{"lang":"eng","text":"We entangled microwave and optical photons for the first time as verified by a measured two-mode vacuum squeezing of 0.7 dB. This electro-optic entanglement is the key resource needed to connect cryogenic quantum circuits."}],"year":"2023"},{"oa_version":"None","author":[{"first_name":"Douglas","id":"0eed2d40-3d48-11ec-8d38-f789cc2e40b2","last_name":"Feitosa Tomé","full_name":"Feitosa Tomé, Douglas"}],"publisher":"Zenodo","doi":"10.5281/ZENODO.10251087","date_created":"2024-01-29T09:06:43Z","has_accepted_license":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.10251087"}],"citation":{"apa":"Feitosa Tomé, D. (2023). douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.10251087\">https://doi.org/10.5281/ZENODO.10251087</a>","ama":"Feitosa Tomé D. douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.10251087\">10.5281/ZENODO.10251087</a>","short":"D. Feitosa Tomé, (2023).","ista":"Feitosa Tomé D. 2023. douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.10251087\">10.5281/ZENODO.10251087</a>.","mla":"Feitosa Tomé, Douglas. <i>Douglastome/Dynamic-Engrams: Dynamic and Selective Engrams Emerge with Memory Consolidation</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.10251087\">10.5281/ZENODO.10251087</a>.","chicago":"Feitosa Tomé, Douglas. “Douglastome/Dynamic-Engrams: Dynamic and Selective Engrams Emerge with Memory Consolidation.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.10251087\">https://doi.org/10.5281/ZENODO.10251087</a>.","ieee":"D. Feitosa Tomé, “douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation.” Zenodo, 2023."},"status":"public","month":"12","oa":1,"related_material":{"record":[{"id":"14887","relation":"used_in_publication","status":"public"}]},"date_published":"2023-12-02T00:00:00Z","corr_author":"1","title":"douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation","ddc":["570"],"article_processing_charge":"No","day":"02","type":"research_data_reference","_id":"14892","department":[{"_id":"TiVo"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"abstract":[{"text":"Code and data necessary to reproduce the simulations and data analyses reported in our manuscript: Tomé, D.F., Zhang, Y., Aida, T., Mosto, O., Lu, Y., Chen, M., Sadeh, S., Roy, D. S., Clopath, C. Dynamic and selective engrams emerge with memory consolidation. 2023.","lang":"eng"}],"date_updated":"2025-04-23T07:40:21Z","year":"2023"},{"citation":{"mla":"Shaw, Thomas, et al. <i>Air Temperature and Near-Surface Meteorology Datasets on Three Swiss Glaciers - Extreme 2022 Summer</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.8277285\">10.5281/ZENODO.8277285</a>.","ista":"Shaw T, Buri P, McCarthy M, Miles E, Pellicciotti F. 2023. Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.8277285\">10.5281/ZENODO.8277285</a>.","ieee":"T. Shaw, P. Buri, M. McCarthy, E. Miles, and F. Pellicciotti, “Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer.” Zenodo, 2023.","chicago":"Shaw, Thomas, Pascal Buri, Michael McCarthy, Evan Miles, and Francesca Pellicciotti. “Air Temperature and Near-Surface Meteorology Datasets on Three Swiss Glaciers - Extreme 2022 Summer.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.8277285\">https://doi.org/10.5281/ZENODO.8277285</a>.","short":"T. Shaw, P. Buri, M. McCarthy, E. Miles, F. Pellicciotti, (2023).","ama":"Shaw T, Buri P, McCarthy M, Miles E, Pellicciotti F. Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.8277285\">10.5281/ZENODO.8277285</a>","apa":"Shaw, T., Buri, P., McCarthy, M., Miles, E., &#38; Pellicciotti, F. (2023). Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.8277285\">https://doi.org/10.5281/ZENODO.8277285</a>"},"status":"public","month":"08","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/ZENODO.8277285"}],"corr_author":"1","date_published":"2023-08-23T00:00:00Z","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"14885"}]},"publisher":"Zenodo","doi":"10.5281/ZENODO.8277285","date_created":"2024-01-31T12:08:26Z","oa_version":"Published Version","author":[{"first_name":"Thomas","id":"3caa3f91-1f03-11ee-96ce-e0e553054d6e","full_name":"Shaw, Thomas","last_name":"Shaw","orcid":"0000-0001-7640-6152"},{"id":"317987aa-9421-11ee-ac5a-b941b041abba","first_name":"Pascal","last_name":"Buri","full_name":"Buri, Pascal"},{"first_name":"Michael","full_name":"McCarthy, Michael","last_name":"McCarthy"},{"full_name":"Miles, Evan","last_name":"Miles","first_name":"Evan"},{"full_name":"Pellicciotti, Francesca","last_name":"Pellicciotti","orcid":"0000-0002-5554-8087","first_name":"Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70"}],"department":[{"_id":"FrPe"}],"_id":"14919","date_updated":"2025-09-04T11:58:38Z","abstract":[{"lang":"eng","text":"GLACIER METEOROLOGICAL DATA SWISS ALPS -2022\r\n"}],"year":"2023","day":"23","article_processing_charge":"No","title":"Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer","ddc":["550"],"type":"research_data_reference"},{"file_date_updated":"2024-02-05T10:19:35Z","language":[{"iso":"eng"}],"has_accepted_license":"1","volume":2,"month":"02","publication":"TheoretiCS","oa_version":"Published Version","publication_identifier":{"issn":["2751-4838"]},"project":[{"grant_number":"101020093","call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","name":"Vigilant Algorithmic Monitoring of Software"}],"article_type":"original","date_updated":"2025-04-14T07:55:57Z","department":[{"_id":"ToHe"}],"quality_controlled":"1","type":"journal_article","article_number":"4","arxiv":1,"external_id":{"arxiv":["2202.07480"]},"ddc":["000"],"title":"Fast symbolic algorithms for mega-regular games under strong transition fairness","ec_funded":1,"date_published":"2023-02-24T00:00:00Z","corr_author":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"status":"public","citation":{"chicago":"Banerjee, Tamajit, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck, and Sadegh Soudjani. “Fast Symbolic Algorithms for Mega-Regular Games under Strong Transition Fairness.” <i>TheoretiCS</i>. EPI Sciences, 2023. <a href=\"https://doi.org/10.46298/theoretics.23.4\">https://doi.org/10.46298/theoretics.23.4</a>.","ieee":"T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, and S. Soudjani, “Fast symbolic algorithms for mega-regular games under strong transition fairness,” <i>TheoretiCS</i>, vol. 2. EPI Sciences, 2023.","ista":"Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. 2023. Fast symbolic algorithms for mega-regular games under strong transition fairness. TheoretiCS. 2, 4.","mla":"Banerjee, Tamajit, et al. “Fast Symbolic Algorithms for Mega-Regular Games under Strong Transition Fairness.” <i>TheoretiCS</i>, vol. 2, 4, EPI Sciences, 2023, doi:<a href=\"https://doi.org/10.46298/theoretics.23.4\">10.46298/theoretics.23.4</a>.","short":"T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, S. Soudjani, TheoretiCS 2 (2023).","apa":"Banerjee, T., Majumdar, R., Mallik, K., Schmuck, A.-K., &#38; Soudjani, S. (2023). Fast symbolic algorithms for mega-regular games under strong transition fairness. <i>TheoretiCS</i>. EPI Sciences. <a href=\"https://doi.org/10.46298/theoretics.23.4\">https://doi.org/10.46298/theoretics.23.4</a>","ama":"Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. Fast symbolic algorithms for mega-regular games under strong transition fairness. <i>TheoretiCS</i>. 2023;2. doi:<a href=\"https://doi.org/10.46298/theoretics.23.4\">10.46298/theoretics.23.4</a>"},"file":[{"relation":"main_file","access_level":"open_access","checksum":"2972d531122a6f15727b396110fb3f5c","content_type":"application/pdf","creator":"dernst","success":1,"date_created":"2024-02-05T10:19:35Z","file_id":"14940","date_updated":"2024-02-05T10:19:35Z","file_size":917076,"file_name":"2023_TheoretiCS_Banerjee.pdf"}],"publication_status":"published","author":[{"first_name":"Tamajit","full_name":"Banerjee, Tamajit","last_name":"Banerjee"},{"first_name":"Rupak","last_name":"Majumdar","full_name":"Majumdar, Rupak"},{"first_name":"Kaushik","id":"0834ff3c-6d72-11ec-94e0-b5b0a4fb8598","full_name":"Mallik, Kaushik","last_name":"Mallik","orcid":"0000-0001-9864-7475"},{"first_name":"Anne-Kathrin","full_name":"Schmuck, Anne-Kathrin","last_name":"Schmuck"},{"first_name":"Sadegh","full_name":"Soudjani, Sadegh","last_name":"Soudjani"}],"intvolume":"         2","date_created":"2024-01-31T13:40:49Z","doi":"10.46298/theoretics.23.4","publisher":"EPI Sciences","year":"2023","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"We consider fixpoint algorithms for two-player games on graphs with $\\omega$-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness, which requires that any execution is strongly fair with respect to a specified set of live edges: whenever the\r\nsource vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well. We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for $\\omega$-regular games -- the new algorithms have the same alternation depth as the classical algorithms but invoke a new type of predecessor operator. For Rabin games with $k$ pairs, the complexity of the new algorithm is $O(n^{k+2}k!)$ symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. Further, we show that GR(1) specifications with strong transition fairness assumptions can be solved with a 3-nested fixpoint algorithm, same as the usual algorithm. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions. We get symbolic algorithms for (generalized) Rabin, parity and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic\r\n$\\omega$-regular games that runs in $O(n^{k+2}k!)$ symbolic steps, improving the state of the art. Finally, we have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude."}],"_id":"14920","acknowledgement":"A previous version of this paper has appeared in TACAS 2022. Authors ordered alphabetically. T. Banerjee was interning with MPI-SWS when this research was conducted. R. Majumdar and A.-K. Schmuck are partially supported by DFG project 389792660 TRR 248–CPEC. A.-K. Schmuck is additionally funded through DFG project (SCHM 3541/1-1). K. Mallik is supported by the ERC project ERC-2020-AdG 101020093.","article_processing_charge":"Yes","day":"24"},{"author":[{"id":"9583e921-e1ad-11ec-9862-cef099626dc9","first_name":"Amedeo Roberto","last_name":"Esposito","full_name":"Esposito, Amedeo Roberto"},{"first_name":"Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","orcid":"0000-0002-3242-7020","last_name":"Mondelli","full_name":"Mondelli, Marco"}],"doi":"10.1109/isit54713.2023.10206899","date_created":"2024-02-02T11:18:40Z","publisher":"IEEE","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2303.07245","open_access":"1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"status":"public","publication_status":"published","citation":{"short":"A.R. Esposito, M. Mondelli, in:, Proceedings of 2023 IEEE International Symposium on Information Theory, IEEE, 2023, pp. 400–405.","apa":"Esposito, A. R., &#38; Mondelli, M. (2023). Concentration without independence via information measures. In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i> (pp. 400–405). Taipei, Taiwan: IEEE. <a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">https://doi.org/10.1109/isit54713.2023.10206899</a>","ama":"Esposito AR, Mondelli M. Concentration without independence via information measures. In: <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. IEEE; 2023:400-405. doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">10.1109/isit54713.2023.10206899</a>","mla":"Esposito, Amedeo Roberto, and Marco Mondelli. “Concentration without Independence via Information Measures.” <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, IEEE, 2023, pp. 400–05, doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">10.1109/isit54713.2023.10206899</a>.","ista":"Esposito AR, Mondelli M. 2023. Concentration without independence via information measures. Proceedings of 2023 IEEE International Symposium on Information Theory. ISIT: International Symposium on Information Theory, 400–405.","chicago":"Esposito, Amedeo Roberto, and Marco Mondelli. “Concentration without Independence via Information Measures.” In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, 400–405. IEEE, 2023. <a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">https://doi.org/10.1109/isit54713.2023.10206899</a>.","ieee":"A. R. Esposito and M. Mondelli, “Concentration without independence via information measures,” in <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, Taipei, Taiwan, 2023, pp. 400–405."},"corr_author":"1","date_published":"2023-06-30T00:00:00Z","day":"30","article_processing_charge":"No","_id":"14922","scopus_import":"1","acknowledgement":"The authors are partially supported by the 2019 Lopez-Loreta Prize. They would also like to thank Professor Jan Maas for providing valuable suggestions and comments on an early version of the work.","year":"2023","abstract":[{"text":"We propose a novel approach to concentration for non-independent random variables. The main idea is to ``pretend'' that the random variables are independent and pay a multiplicative price measuring how far they are from actually being independent. This price is encapsulated in the Hellinger integral between the joint and the product of the marginals, which is then upper bounded leveraging tensorisation properties. Our bounds represent a natural generalisation of concentration inequalities in the presence of dependence: we recover exactly the classical bounds (McDiarmid's inequality) when the random variables are independent. Furthermore, in a ``large deviations'' regime, we obtain the same decay in the probability as for the independent case, even when the random variables display non-trivial dependencies. To show this, we consider a number of applications of interest. First, we provide a bound for Markov chains with finite state space. Then, we consider the Simple Symmetric Random Walk, which is a non-contracting Markov chain, and a non-Markovian setting in which the stochastic process depends on its entire past. To conclude, we propose an application to Markov Chain Monte Carlo methods, where our approach leads to an improved lower bound on the minimum burn-in period required to reach a certain accuracy. In all of these settings, we provide a regime of parameters in which our bound fares better than what the state of the art can provide.","lang":"eng"}],"page":"400-405","oa_version":"Preprint","publication_identifier":{"eisbn":["9781665475549"],"eissn":["2157-8117"]},"month":"06","publication":"Proceedings of 2023 IEEE International Symposium on Information Theory","related_material":{"record":[{"status":"public","id":"15172","relation":"later_version"}]},"language":[{"iso":"eng"}],"title":"Concentration without independence via information measures","arxiv":1,"external_id":{"arxiv":["2303.07245"]},"conference":{"start_date":"2023-06-25","end_date":"2023-06-30","location":"Taipei, Taiwan","name":"ISIT: International Symposium on Information Theory"},"type":"conference","quality_controlled":"1","department":[{"_id":"MaMo"}],"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"date_updated":"2025-09-04T13:06:52Z"},{"oa_version":"Preprint","publication_identifier":{"isbn":["9781665475549"],"eissn":["2157-8117"]},"language":[{"iso":"eng"}],"publication":"Proceedings of 2023 IEEE International Symposium on Information Theory","month":"06","type":"conference","external_id":{"arxiv":["2302.03306"]},"title":"Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise","arxiv":1,"conference":{"end_date":"2023-06-30","location":"Taipei, Taiwan","start_date":"2023-06-25","name":"ISIT: International Symposium on Information Theory"},"date_updated":"2025-07-10T11:51:04Z","department":[{"_id":"MaMo"}],"quality_controlled":"1","publisher":"IEEE","doi":"10.1109/isit54713.2023.10206671","date_created":"2024-02-02T11:20:39Z","author":[{"last_name":"Fu","full_name":"Fu, Teng","first_name":"Teng"},{"first_name":"YuHao","last_name":"Liu","full_name":"Liu, YuHao"},{"first_name":"Jean","full_name":"Barbier, Jean","last_name":"Barbier"},{"orcid":"0000-0002-3242-7020","last_name":"Mondelli","full_name":"Mondelli, Marco","first_name":"Marco","id":"27EB676C-8706-11E9-9510-7717E6697425"},{"last_name":"Liang","full_name":"Liang, ShanSuo","first_name":"ShanSuo"},{"first_name":"TianQi","last_name":"Hou","full_name":"Hou, TianQi"}],"date_published":"2023-06-30T00:00:00Z","corr_author":"1","citation":{"mla":"Fu, Teng, et al. “Mismatched Estimation of Non-Symmetric Rank-One Matrices Corrupted by Structured Noise.” <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, IEEE, 2023, pp. 1178–83, doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">10.1109/isit54713.2023.10206671</a>.","ista":"Fu T, Liu Y, Barbier J, Mondelli M, Liang S, Hou T. 2023. Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. Proceedings of 2023 IEEE International Symposium on Information Theory. ISIT: International Symposium on Information Theory, 1178–1183.","ieee":"T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, and T. Hou, “Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise,” in <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, Taipei, Taiwan, 2023, pp. 1178–1183.","chicago":"Fu, Teng, YuHao Liu, Jean Barbier, Marco Mondelli, ShanSuo Liang, and TianQi Hou. “Mismatched Estimation of Non-Symmetric Rank-One Matrices Corrupted by Structured Noise.” In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, 1178–83. IEEE, 2023. <a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">https://doi.org/10.1109/isit54713.2023.10206671</a>.","apa":"Fu, T., Liu, Y., Barbier, J., Mondelli, M., Liang, S., &#38; Hou, T. (2023). Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i> (pp. 1178–1183). Taipei, Taiwan: IEEE. <a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">https://doi.org/10.1109/isit54713.2023.10206671</a>","ama":"Fu T, Liu Y, Barbier J, Mondelli M, Liang S, Hou T. Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In: <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. IEEE; 2023:1178-1183. doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">10.1109/isit54713.2023.10206671</a>","short":"T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, T. Hou, in:, Proceedings of 2023 IEEE International Symposium on Information Theory, IEEE, 2023, pp. 1178–1183."},"publication_status":"published","oa":1,"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2302.03306"}],"article_processing_charge":"No","day":"30","page":"1178-1183","abstract":[{"text":"We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular values. As the signal-to-noise ratio and the noise structure are unknown, a Gaussian setup is incorrectly assumed. We derive the exact analytic expression for the error of the mismatched Bayes estimator and also provide the analysis of an approximate message passing (AMP) algorithm. The first result exploits the asymptotic behavior of spherical integrals for rectangular matrices and of low-rank matrix perturbations; the second one relies on the design and analysis of an auxiliary AMP. The numerical experiments show that there is a performance gap between the AMP and Bayes estimators, which is due to the incorrect estimation of the signal norm.","lang":"eng"}],"year":"2023","scopus_import":"1","_id":"14923"},{"_id":"14924","acknowledgement":"D. Wu and M. Mondelli are partially supported by the 2019 Lopez-Loreta Prize. V. Kungurtsev was supported by the OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 \"Research Center for Informatics\".","year":"2023","abstract":[{"lang":"eng","text":"The stochastic heavy ball method (SHB), also known as stochastic gradient descent (SGD) with Polyak's momentum, is widely used in training neural networks. However, despite the remarkable success of such algorithm in practice, its theoretical characterization remains limited. In this paper, we focus on neural networks with two and three layers and provide a rigorous understanding of the properties of the solutions found by SHB: \\emph{(i)} stability after dropping out part of the neurons, \\emph{(ii)} connectivity along a low-loss path, and \\emph{(iii)} convergence to the global optimum.\r\nTo achieve this goal, we take a mean-field view and relate the SHB dynamics to a certain partial differential equation in the limit of large network widths. This mean-field perspective has inspired a recent line of work focusing on SGD while, in contrast, our paper considers an algorithm with momentum. More specifically, after proving existence and uniqueness of the limit differential equations, we show convergence to the global optimum and give a quantitative bound between the mean-field limit and the SHB dynamics of a finite-width network. Armed with this last bound, we are able to establish the dropout-stability and connectivity of SHB solutions."}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"day":"28","article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2210.06819","open_access":"1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","oa":1,"citation":{"ama":"Wu D, Kungurtsev V, Mondelli M. Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In: <i>Transactions on Machine Learning Research</i>. ML Research Press; 2023.","apa":"Wu, D., Kungurtsev, V., &#38; Mondelli, M. (2023). Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In <i>Transactions on Machine Learning Research</i>. ML Research Press.","short":"D. Wu, V. Kungurtsev, M. Mondelli, in:, Transactions on Machine Learning Research, ML Research Press, 2023.","ista":"Wu D, Kungurtsev V, Mondelli M. 2023. Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. Transactions on Machine Learning Research. , TMLR, .","mla":"Wu, Diyuan, et al. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.” <i>Transactions on Machine Learning Research</i>, ML Research Press, 2023.","chicago":"Wu, Diyuan, Vyacheslav Kungurtsev, and Marco Mondelli. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.” In <i>Transactions on Machine Learning Research</i>. ML Research Press, 2023.","ieee":"D. Wu, V. Kungurtsev, and M. Mondelli, “Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence,” in <i>Transactions on Machine Learning Research</i>, 2023."},"publication_status":"published","corr_author":"1","date_published":"2023-02-28T00:00:00Z","author":[{"last_name":"Wu","full_name":"Wu, Diyuan","first_name":"Diyuan","id":"1a5914c2-896a-11ed-bdf8-fb80621a0635"},{"first_name":"Vyacheslav","last_name":"Kungurtsev","full_name":"Kungurtsev, Vyacheslav"},{"first_name":"Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","last_name":"Mondelli","orcid":"0000-0002-3242-7020"}],"date_created":"2024-02-02T11:21:56Z","publisher":"ML Research Press","quality_controlled":"1","department":[{"_id":"MaMo"}],"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"date_updated":"2026-06-18T17:41:36Z","alternative_title":["TMLR"],"title":"Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence","arxiv":1,"ddc":["000"],"external_id":{"arxiv":["2210.06819"]},"type":"conference","has_accepted_license":"1","publication":"Transactions on Machine Learning Research","month":"02","language":[{"iso":"eng"}],"oa_version":"Published Version"},{"publication_identifier":{"eissn":["2835-8856"]},"oa_version":"Published Version","publication":"Journal of Machine Learning Research","month":"12","has_accepted_license":"1","language":[{"iso":"eng"}],"file_date_updated":"2024-02-07T14:57:32Z","title":"Image retrieval outperforms diffusion models on data augmentation","ddc":["000"],"type":"journal_article","quality_controlled":"1","department":[{"_id":"FrLo"}],"alternative_title":["TMLR"],"article_type":"original","date_updated":"2024-02-12T08:30:21Z","date_created":"2024-02-07T14:57:39Z","publisher":"ML Research Press","author":[{"last_name":"Burg","full_name":"Burg, Max","first_name":"Max"},{"full_name":"Wenzel, Florian","last_name":"Wenzel","first_name":"Florian"},{"first_name":"Dominik","full_name":"Zietlow, Dominik","last_name":"Zietlow"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"last_name":"Makansi","full_name":"Makansi, Osama","first_name":"Osama"},{"full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"},{"first_name":"Chris","full_name":"Russell, Chris","last_name":"Russell"}],"status":"public","oa":1,"publication_status":"published","file":[{"date_created":"2024-02-07T14:57:32Z","creator":"ptazenko","content_type":"application/pdf","checksum":"af87ddea7908923426365347b9c87ba7","access_level":"open_access","relation":"main_file","file_name":"Burg_et_al_2023_Image_retrieval_outperforms.pdf","file_size":27325153,"file_id":"14950","date_updated":"2024-02-07T14:57:32Z"}],"citation":{"chicago":"Burg, Max, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” <i>Journal of Machine Learning Research</i>. ML Research Press, 2023.","ieee":"M. Burg <i>et al.</i>, “Image retrieval outperforms diffusion models on data augmentation,” <i>Journal of Machine Learning Research</i>. ML Research Press, 2023.","mla":"Burg, Max, et al. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” <i>Journal of Machine Learning Research</i>, ML Research Press, 2023.","ista":"Burg M, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. 2023. Image retrieval outperforms diffusion models on data augmentation. Journal of Machine Learning Research.","ama":"Burg M, Wenzel F, Zietlow D, et al. Image retrieval outperforms diffusion models on data augmentation. <i>Journal of Machine Learning Research</i>. 2023.","short":"M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, Journal of Machine Learning Research (2023).","apa":"Burg, M., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &#38; Russell, C. (2023). Image retrieval outperforms diffusion models on data augmentation. <i>Journal of Machine Learning Research</i>. ML Research Press."},"main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=xflYdGZMpv"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2023-12-10T00:00:00Z","day":"10","article_processing_charge":"No","acknowledgement":"The authors would like to thank Varad Gunjal and Vishaal Udandarao. MFB thanks the International Max Planck Research School for Intelligent Systems (IMPRS-IS).","_id":"14949","year":"2023","abstract":[{"lang":"eng","text":"Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large datasets, often with noisy annotations, and it remains an open question to which extent these models contribute to downstream classification performance. In particular, it remains unclear if they generalize enough to improve over directly using the additional data of their pre-training process for augmentation. We systematically evaluate a range of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. Personalizing diffusion models towards the target data outperforms simpler prompting strategies. However, using the pre-training data of the diffusion model alone, via a simple nearest-neighbor retrieval procedure, leads to even stronger downstream performance. Our study explores the potential of diffusion models in generating new training data, and surprisingly finds that these sophisticated models are not yet able to beat a simple and strong image retrieval baseline on simple downstream vision tasks."}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"}},{"acknowledgement":"We are thankful to the reviewers for providing constructive feedback and Kun Zhang and Dominik Janzing for helpful discussion on the special case of deterministic children. This work was supported by Hasler Foundation Program: Hasler Responsible AI (project number 21043). This work was supported by the Swiss National Science Foundation (SNSF) under grant number 200021_205011. Francesco Locatello did not contribute to this work at Amazon. ","department":[{"_id":"FrLo"}],"_id":"14953","date_updated":"2024-02-12T09:45:58Z","abstract":[{"text":"This paper provides statistical sample complexity bounds for score-matching and its applications in causal discovery. We demonstrate that accurate estimation of the score function is achievable by training a standard deep ReLU neural network using stochastic gradient descent. We establish bounds on the error rate of recovering causal relationships using the score-matching-based causal discovery method of Rolland et al. [2022], assuming a sufficiently good estimation of the score function. Finally, we analyze the upper bound of score-matching estimation within the score-based generative modeling, which has been applied for causal discovery but is also of independent interest within the domain of generative models.","lang":"eng"}],"year":"2023","article_processing_charge":"No","day":"27","title":"Sample complexity bounds for score-matching: Causal discovery and generative modeling","article_number":"2310.18123","external_id":{"arxiv":["2310.18123"]},"arxiv":1,"type":"preprint","citation":{"mla":"Zhu, Zhenyu, et al. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.” <i>ArXiv</i>, 2310.18123, doi:<a href=\"https://doi.org/10.48550/arXiv.2310.18123\">10.48550/arXiv.2310.18123</a>.","ista":"Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv, 2310.18123.","chicago":"Zhu, Zhenyu, Francesco Locatello, and Volkan Cevher. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2310.18123\">https://doi.org/10.48550/arXiv.2310.18123</a>.","ieee":"Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” <i>arXiv</i>. .","short":"Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).","ama":"Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2310.18123\">10.48550/arXiv.2310.18123</a>","apa":"Zhu, Z., Locatello, F., &#38; Cevher, V. (n.d.). Sample complexity bounds for score-matching: Causal discovery and generative modeling. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2310.18123\">https://doi.org/10.48550/arXiv.2310.18123</a>"},"publication_status":"submitted","oa":1,"status":"public","month":"10","publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.18123","open_access":"1"}],"language":[{"iso":"eng"}],"date_published":"2023-10-27T00:00:00Z","date_created":"2024-02-07T15:11:11Z","doi":"10.48550/arXiv.2310.18123","oa_version":"Preprint","author":[{"full_name":"Zhu, Zhenyu","last_name":"Zhu","first_name":"Zhenyu"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"last_name":"Cevher","full_name":"Cevher, Volkan","first_name":"Volkan"}]},{"day":"20","article_processing_charge":"No","arxiv":1,"title":"Assumption violations in causal discovery and the robustness of score matching","external_id":{"arxiv":["2310.13387"]},"article_number":"2310.13387","type":"preprint","department":[{"_id":"FrLo"}],"acknowledgement":"We thank Kun Zhang and Carl-Johann Simon-Gabriel for the insightful discussions. This work\r\nhas been supported by AFOSR, grant n. FA8655-20-1-7035. FM is supported by Programma\r\nOperativo Nazionale ricerca e innovazione 2014-2020. FM partially contributed to this work during an internship at Amazon Web Services with FL. FL partially contributed while at AWS.","_id":"14954","year":"2023","abstract":[{"text":"When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recover the causal structure, exploiting the statistical properties of their data. Because causal discovery without further assumptions is an ill-posed problem, each algorithm comes with its own set of\r\nusually untestable assumptions, some of which are hard to meet in real datasets. Motivated by these considerations, this paper extensively benchmarks the empirical performance of recent causal discovery methods on observational i.i.d. data generated under different background conditions, allowing for violations of the critical assumptions required by each selected approach. Our experimental findings show that score matching-based methods demonstrate\r\nsurprising performance in the false positive and false negative rate of the inferred graph in these challenging scenarios, and we provide theoretical insights into their performance. This work is also the first effort to benchmark the stability of causal discovery algorithms with respect to the values of their hyperparameters. Finally, we hope this paper will set a new standard for the evaluation of causal discovery methods and can serve as an accessible entry point for practitioners interested in the field, highlighting the empirical implications of different algorithm choices.","lang":"eng"}],"date_updated":"2024-02-12T09:51:15Z","date_created":"2024-02-07T15:11:56Z","doi":"10.48550/arXiv.2310.13387","author":[{"first_name":"Francesco","full_name":"Montagna, Francesco","last_name":"Montagna"},{"full_name":"Mastakouri, Atalanti A.","last_name":"Mastakouri","first_name":"Atalanti A."},{"first_name":"Elias","last_name":"Eulig","full_name":"Eulig, Elias"},{"first_name":"Nicoletta","last_name":"Noceti","full_name":"Noceti, Nicoletta"},{"first_name":"Lorenzo","full_name":"Rosasco, Lorenzo","last_name":"Rosasco"},{"first_name":"Dominik","full_name":"Janzing, Dominik","last_name":"Janzing"},{"full_name":"Aragam, Bryon","last_name":"Aragam","first_name":"Bryon"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco"}],"oa_version":"Preprint","oa":1,"publication":"arXiv","status":"public","month":"10","publication_status":"submitted","citation":{"ama":"Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal discovery and the robustness of score matching. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2310.13387\">10.48550/arXiv.2310.13387</a>","apa":"Montagna, F., Mastakouri, A. A., Eulig, E., Noceti, N., Rosasco, L., Janzing, D., … Locatello, F. (n.d.). Assumption violations in causal discovery and the robustness of score matching. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2310.13387\">https://doi.org/10.48550/arXiv.2310.13387</a>","short":"F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello, ArXiv (n.d.).","mla":"Montagna, Francesco, et al. “Assumption Violations in Causal Discovery and the Robustness of Score Matching.” <i>ArXiv</i>, 2310.13387, doi:<a href=\"https://doi.org/10.48550/arXiv.2310.13387\">10.48550/arXiv.2310.13387</a>.","ista":"Montagna F, Mastakouri AA, Eulig E, Noceti N, Rosasco L, Janzing D, Aragam B, Locatello F. Assumption violations in causal discovery and the robustness of score matching. arXiv, 2310.13387.","chicago":"Montagna, Francesco, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, and Francesco Locatello. “Assumption Violations in Causal Discovery and the Robustness of Score Matching.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2310.13387\">https://doi.org/10.48550/arXiv.2310.13387</a>.","ieee":"F. Montagna <i>et al.</i>, “Assumption violations in causal discovery and the robustness of score matching,” <i>arXiv</i>. ."},"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.13387","open_access":"1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2023-10-20T00:00:00Z","language":[{"iso":"eng"}]},{"oa_version":"Published Version","has_accepted_license":"1","publication":"Causal Representation Learning Workshop at NeurIPS 2023","month":"12","file_date_updated":"2024-02-13T08:50:53Z","language":[{"iso":"eng"}],"title":"A sparsity principle for partially observable causal representation learning","ddc":["000"],"article_number":"54","conference":{"name":"CRL: Causal Representation Learning Workshop at NeurIPS","start_date":"2023-12-15","end_date":"2023-12-15","location":"New Orleans, LA, United States"},"type":"conference","department":[{"_id":"FrLo"}],"quality_controlled":"1","date_updated":"2025-02-04T12:37:34Z","OA_place":"repository","author":[{"full_name":"Xu, Danru","last_name":"Xu","first_name":"Danru"},{"first_name":"Dingling","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling","last_name":"Yao"},{"first_name":"Sebastien","last_name":"Lachapelle","full_name":"Lachapelle, Sebastien"},{"first_name":"Perouz","last_name":"Taslakian","full_name":"Taslakian, Perouz"},{"first_name":"Julius","full_name":"von Kügelgen, Julius","last_name":"von Kügelgen"},{"last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"},{"last_name":"Magliacane","full_name":"Magliacane, Sara","first_name":"Sara"}],"publisher":"OpenReview","date_created":"2024-02-07T15:17:51Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://openreview.net/forum?id=Whr6uobelR","open_access":"1"}],"citation":{"ama":"Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. OpenReview; 2023.","apa":"Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., &#38; Magliacane, S. (2023). A sparsity principle for partially observable causal representation learning. In <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. New Orleans, LA, United States: OpenReview.","short":"D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview, 2023.","chicago":"Xu, Danru, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, and Sara Magliacane. “A Sparsity Principle for Partially Observable Causal Representation Learning.” In <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. OpenReview, 2023.","ieee":"D. Xu <i>et al.</i>, “A sparsity principle for partially observable causal representation learning,” in <i>Causal Representation Learning Workshop at NeurIPS 2023</i>, New Orleans, LA, United States, 2023.","ista":"Xu D, Yao D, Lachapelle S, Taslakian P, von Kügelgen J, Locatello F, Magliacane S. 2023. A sparsity principle for partially observable causal representation learning. Causal Representation Learning Workshop at NeurIPS 2023. CRL: Causal Representation Learning Workshop at NeurIPS, 54.","mla":"Xu, Danru, et al. “A Sparsity Principle for Partially Observable Causal Representation Learning.” <i>Causal Representation Learning Workshop at NeurIPS 2023</i>, 54, OpenReview, 2023."},"publication_status":"published","file":[{"file_size":552357,"file_name":"2023_CRL_Xu.pdf","date_updated":"2024-02-13T08:50:53Z","file_id":"14982","relation":"main_file","checksum":"484efc27bda75ed6666044989695d9b6","access_level":"open_access","content_type":"application/pdf","success":1,"creator":"dernst","date_created":"2024-02-13T08:50:53Z"}],"oa":1,"status":"public","date_published":"2023-12-05T00:00:00Z","article_processing_charge":"No","day":"05","OA_type":"green","_id":"14958","acknowledgement":"This work was initiated at the Second Bellairs Workshop on Causality held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop participants for providing a stimulating research environment. The research of DX and SM was supported by the Air Force Office of Scientific Research under award number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force. We also thank SURF for the support in using the Dutch National Supercomputer Snellius. DY was supported by an Amazon fellowship and the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Work done outside of Amazon. SL was supported by an IVADO excellence PhD scholarship and by Samsung Electronics Co., Ldt. JvK acknowledges support from the German Federal Ministry of Education and Research (BMBF)\r\nthrough the Tübingen AI Center (FKZ: 01IS18039B).\r\n","abstract":[{"lang":"eng","text":"Causal representation learning (CRL) aims at identifying high-level causal variables from low-level data, e.g. images. Current methods usually assume that all causal variables are captured in the high-dimensional observations. In this work, we focus on learning causal representations from data under partial observability, i.e., when some of the causal variables are not observed in the measurements, and the set of masked variables changes across the different samples. We introduce some initial theoretical results for identifying causal variables under partial observability by exploiting a sparsity regularizer, focusing in particular on the linear and piecewise linear mixing function case. We provide a theorem that allows us to identify the causal variables up to permutation and element-wise linear transformations in the linear case and a lemma that allows us to identify causal variables up to linear transformation in the piecewise case. Finally, we provide a conjecture that would allow us to identify the causal variables up to permutation and element-wise linear transformations also in the piecewise linear case. We test the theorem and conjecture on simulated data, showing the effectiveness of our method."}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"year":"2023"},{"article_processing_charge":"No","day":"22","external_id":{"arxiv":["2310.14246"]},"article_number":"2310.14246","title":"Shortcuts for causal discovery of nonlinear models by score matching","arxiv":1,"type":"preprint","department":[{"_id":"FrLo"}],"_id":"14961","date_updated":"2024-10-09T21:08:10Z","abstract":[{"lang":"eng","text":"The use of simulated data in the field of causal discovery is ubiquitous due to the scarcity of annotated real data. Recently, Reisach et al., 2021 highlighted the emergence of patterns in simulated linear data, which displays increasing marginal variance in the casual direction. As an ablation in their experiments, Montagna et al., 2023 found that similar patterns may emerge in\r\nnonlinear models for the variance of the score vector $\\nabla \\log p_{\\mathbf{X}}$, and introduced the ScoreSort algorithm. In this work, we formally define and characterize this score-sortability pattern of nonlinear additive noise models. We find that it defines a class of identifiable (bivariate) causal models overlapping with nonlinear additive noise models. We\r\ntheoretically demonstrate the advantages of ScoreSort in terms of statistical efficiency compared to prior state-of-the-art score matching-based methods and empirically show the score-sortability of the most common synthetic benchmarks in the literature. Our findings remark (1) the lack of diversity in the data as an important limitation in the evaluation of nonlinear causal discovery approaches, (2) the importance of thoroughly testing different settings within a problem class, and (3) the importance of analyzing statistical properties in\r\ncausal discovery, where research is often limited to defining identifiability conditions of the model. "}],"year":"2023","date_created":"2024-02-08T15:31:46Z","doi":"10.48550/arXiv.2310.14246","oa_version":"Preprint","author":[{"last_name":"Montagna","full_name":"Montagna, Francesco","first_name":"Francesco"},{"last_name":"Noceti","full_name":"Noceti, Nicoletta","first_name":"Nicoletta"},{"last_name":"Rosasco","full_name":"Rosasco, Lorenzo","first_name":"Lorenzo"},{"orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"}],"citation":{"ieee":"F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal discovery of nonlinear models by score matching,” <i>arXiv</i>. .","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, and Francesco Locatello. “Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2310.14246\">https://doi.org/10.48550/arXiv.2310.14246</a>.","ista":"Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. arXiv, 2310.14246.","mla":"Montagna, Francesco, et al. “Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.” <i>ArXiv</i>, 2310.14246, doi:<a href=\"https://doi.org/10.48550/arXiv.2310.14246\">10.48550/arXiv.2310.14246</a>.","short":"F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).","apa":"Montagna, F., Noceti, N., Rosasco, L., &#38; Locatello, F. (n.d.). Shortcuts for causal discovery of nonlinear models by score matching. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2310.14246\">https://doi.org/10.48550/arXiv.2310.14246</a>","ama":"Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2310.14246\">10.48550/arXiv.2310.14246</a>"},"publication_status":"submitted","status":"public","month":"10","oa":1,"publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2310.14246"}],"language":[{"iso":"eng"}],"corr_author":"1","date_published":"2023-10-22T00:00:00Z"}]