[{"file_date_updated":"2023-01-30T10:48:37Z","department":[{"_id":"BeBi"}],"main_file_link":[{"url":"https://bmvc2022.mpi-inf.mpg.de/708/","open_access":"1"}],"abstract":[{"lang":"eng","text":"Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry and illumination is critical for obtaining photorealistic results. Current methods are unable to explicitly model in 3D while handing both viewpoint and illumination editing from a single image. In this paper, we propose VoRF, a novel approach that can take even a single portrait image as input and relight human heads under novel illuminations that can be viewed from arbitrary viewpoints. VoRF represents a human head as a continuous volumetric field and learns a prior model of human heads using a coordinate-based MLP with separate latent spaces for identity and illumination. The prior model is learnt in an auto-decoder manner over a diverse class of head shapes and appearances, allowing VoRF to generalize to novel test identities from a single input image. Additionally, VoRF has a reflectance MLP that uses the intermediate features of the prior model for rendering One-Light-at-A-Time (OLAT) images under novel views. We synthesize novel illuminations by combining these OLAT images with target environment maps. Qualitative and quantitative evaluations demonstrate the effectiveness of VoRF for relighting and novel view synthesis even when applied to unseen subjects under uncontrolled illuminations."}],"article_processing_charge":"No","publisher":"British Machine Vision Association and Society for Pattern Recognition","has_accepted_license":"1","author":[{"last_name":"Rao","first_name":"Pramod","full_name":"Rao, Pramod"},{"full_name":"B R, Mallikarjun","first_name":"Mallikarjun","last_name":"B R"},{"full_name":"Fox, Gereon","first_name":"Gereon","last_name":"Fox"},{"first_name":"Tim","full_name":"Weyrich, Tim","last_name":"Weyrich"},{"first_name":"Bernd","full_name":"Bickel, Bernd","orcid":"0000-0001-6511-9385","id":"49876194-F248-11E8-B48F-1D18A9856A87","last_name":"Bickel"},{"last_name":"Seidel","full_name":"Seidel, Hans-Peter","first_name":"Hans-Peter"},{"first_name":"Hanspeter","full_name":"Pfister, Hanspeter","last_name":"Pfister"},{"last_name":"Matusik","full_name":"Matusik, Wojciech","first_name":"Wojciech"},{"first_name":"Ayush","full_name":"Tewari, Ayush","last_name":"Tewari"},{"full_name":"Theobalt, Christian","first_name":"Christian","last_name":"Theobalt"},{"last_name":"Elgharib","first_name":"Mohamed","full_name":"Elgharib, Mohamed"}],"ddc":["000"],"citation":{"mla":"Rao, Pramod, et al. “VoRF: Volumetric Relightable Faces.” <i>33rd British Machine Vision Conference</i>, 708, British Machine Vision Association and Society for Pattern Recognition, 2022.","chicago":"Rao, Pramod, Mallikarjun B R, Gereon Fox, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, et al. “VoRF: Volumetric Relightable Faces.” In <i>33rd British Machine Vision Conference</i>. British Machine Vision Association and Society for Pattern Recognition, 2022.","ieee":"P. Rao <i>et al.</i>, “VoRF: Volumetric Relightable Faces,” in <i>33rd British Machine Vision Conference</i>, London, United Kingdom, 2022.","apa":"Rao, P., B R, M., Fox, G., Weyrich, T., Bickel, B., Seidel, H.-P., … Elgharib, M. (2022). VoRF: Volumetric Relightable Faces. In <i>33rd British Machine Vision Conference</i>. London, United Kingdom: British Machine Vision Association and Society for Pattern Recognition.","ama":"Rao P, B R M, Fox G, et al. VoRF: Volumetric Relightable Faces. In: <i>33rd British Machine Vision Conference</i>. British Machine Vision Association and Society for Pattern Recognition; 2022.","short":"P. Rao, M. B R, G. Fox, T. Weyrich, B. Bickel, H.-P. Seidel, H. Pfister, W. Matusik, A. Tewari, C. Theobalt, M. Elgharib, in:, 33rd British Machine Vision Conference, British Machine Vision Association and Society for Pattern Recognition, 2022.","ista":"Rao P, B R M, Fox G, Weyrich T, Bickel B, Seidel H-P, Pfister H, Matusik W, Tewari A, Theobalt C, Elgharib M. 2022. VoRF: Volumetric Relightable Faces. 33rd British Machine Vision Conference. BMVC: British Machine Vision Conference, 708."},"status":"public","date_created":"2023-01-30T10:47:06Z","conference":{"location":"London, United Kingdom","end_date":"2022-11-24","start_date":"2022-11-21","name":"BMVC: British Machine Vision Conference"},"quality_controlled":"1","month":"12","date_updated":"2023-10-31T08:40:55Z","publication_status":"published","year":"2022","oa_version":"Published Version","_id":"12452","file":[{"checksum":"b60b70bb48700aee709c85a69231821d","access_level":"open_access","content_type":"application/pdf","file_size":5202710,"creator":"bbickel","file_name":"vorf_main.pdf","relation":"main_file","date_updated":"2023-01-30T10:48:18Z","file_id":"12453","date_created":"2023-01-30T10:48:18Z","title":"VoRF: Volumetric Relightable Faces"},{"date_created":"2023-01-30T10:48:29Z","title":"VoRF: Volumetric Relightable Faces – SUPPLEMENTAL MATERIAL –","checksum":"ce5f4ce66eaaa1590ee5df989fca6f61","access_level":"open_access","content_type":"application/pdf","file_size":37953188,"creator":"bbickel","relation":"supplementary_material","file_name":"vorf_supp.pdf","date_updated":"2023-01-30T10:48:29Z","file_id":"12454"},{"date_created":"2023-01-30T10:48:37Z","access_level":"open_access","checksum":"08aecca434b08fee75ee1efe87943718","content_type":"video/mp4","file_size":57855492,"creator":"bbickel","relation":"supplementary_material","file_name":"video.mp4","date_updated":"2023-01-30T10:48:37Z","file_id":"12455"}],"type":"conference","language":[{"iso":"eng"}],"acknowledgement":"This work was supported by the ERC Consolidator Grant 4DReply (770784).","article_number":"708","title":"VoRF: Volumetric Relightable Faces","day":"01","publication":"33rd British Machine Vision Conference","scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-12-01T00:00:00Z","oa":1},{"has_accepted_license":"1","publisher":"IOP Publishing","file_date_updated":"2023-02-02T08:35:52Z","department":[{"_id":"MaMo"}],"abstract":[{"lang":"eng","text":"We consider the problem of estimating a signal from measurements obtained via a generalized linear model. We focus on estimators based on approximate message passing (AMP), a family of iterative algorithms with many appealing features: the performance of AMP in the high-dimensional limit can be succinctly characterized under suitable model assumptions; AMP can also be tailored to the empirical distribution of the signal entries, and for a wide class of estimation problems, AMP is conjectured to be optimal among all polynomial-time algorithms. However, a major issue of AMP is that in many models (such as phase retrieval), it requires an initialization correlated with the ground-truth signal and independent from the measurement matrix. Assuming that such an initialization is available is typically not realistic. In this paper, we solve this problem by proposing an AMP algorithm initialized with a spectral estimator. With such an initialization, the standard AMP analysis fails since the spectral estimator depends in a complicated way on the design matrix. Our main contribution is a rigorous characterization of the performance of AMP with spectral initialization in the high-dimensional limit. The key technical idea is to define and analyze a two-phase artificial AMP algorithm that first produces the spectral estimator, and then closely approximates the iterates of the true AMP. We also provide numerical results that demonstrate the validity of the proposed approach."}],"article_processing_charge":"Yes (via OA deal)","ddc":["510","530"],"citation":{"ista":"Mondelli M, Venkataramanan R. 2022. Approximate message passing with spectral initialization for generalized linear models. Journal of Statistical Mechanics: Theory and Experiment. 2022(11), 114003.","short":"M. Mondelli, R. Venkataramanan, Journal of Statistical Mechanics: Theory and Experiment 2022 (2022).","mla":"Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with Spectral Initialization for Generalized Linear Models.” <i>Journal of Statistical Mechanics: Theory and Experiment</i>, vol. 2022, no. 11, 114003, IOP Publishing, 2022, doi:<a href=\"https://doi.org/10.1088/1742-5468/ac9828\">10.1088/1742-5468/ac9828</a>.","chicago":"Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with Spectral Initialization for Generalized Linear Models.” <i>Journal of Statistical Mechanics: Theory and Experiment</i>. IOP Publishing, 2022. <a href=\"https://doi.org/10.1088/1742-5468/ac9828\">https://doi.org/10.1088/1742-5468/ac9828</a>.","ieee":"M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral initialization for generalized linear models,” <i>Journal of Statistical Mechanics: Theory and Experiment</i>, vol. 2022, no. 11. IOP Publishing, 2022.","apa":"Mondelli, M., &#38; Venkataramanan, R. (2022). Approximate message passing with spectral initialization for generalized linear models. <i>Journal of Statistical Mechanics: Theory and Experiment</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1742-5468/ac9828\">https://doi.org/10.1088/1742-5468/ac9828</a>","ama":"Mondelli M, Venkataramanan R. Approximate message passing with spectral initialization for generalized linear models. <i>Journal of Statistical Mechanics: Theory and Experiment</i>. 2022;2022(11). doi:<a href=\"https://doi.org/10.1088/1742-5468/ac9828\">10.1088/1742-5468/ac9828</a>"},"intvolume":"      2022","doi":"10.1088/1742-5468/ac9828","author":[{"last_name":"Mondelli","orcid":"0000-0002-3242-7020","id":"27EB676C-8706-11E9-9510-7717E6697425","first_name":"Marco","full_name":"Mondelli, Marco"},{"last_name":"Venkataramanan","full_name":"Venkataramanan, Ramji","first_name":"Ramji"}],"external_id":{"isi":["000889589900001"]},"quality_controlled":"1","status":"public","date_created":"2023-02-02T08:31:57Z","issue":"11","date_updated":"2025-04-15T07:50:16Z","publication_status":"published","month":"11","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"keyword":["Statistics","Probability and Uncertainty","Statistics and Probability","Statistical and Nonlinear Physics"],"article_type":"original","oa_version":"Published Version","_id":"12480","year":"2022","language":[{"iso":"eng"}],"acknowledgement":"The authors would like to thank Andrea Montanari for helpful discussions.\r\nM Mondelli was partially supported by the 2019 Lopez-Loreta Prize. R Venkataramanan was partially supported by the Alan Turing Institute under the EPSRC Grant\r\nEP/N510129/1.","volume":2022,"type":"journal_article","file":[{"date_updated":"2023-02-02T08:35:52Z","file_id":"12481","relation":"main_file","file_name":"2022_JourStatisticalMechanics_Mondelli.pdf","file_size":1729997,"creator":"dernst","access_level":"open_access","checksum":"01411ffa76d3e380a0446baeb89b1ef7","content_type":"application/pdf","success":1,"date_created":"2023-02-02T08:35:52Z"}],"publication":"Journal of Statistical Mechanics: Theory and Experiment","title":"Approximate message passing with spectral initialization for generalized linear models","article_number":"114003","day":"24","fulldoi":"https://doi.org/10.1088/1742-5468/ac9828","date_published":"2022-11-24T00:00:00Z","oa":1,"isi":1,"publication_identifier":{"issn":["1742-5468"]},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","scopus_import":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"corr_author":"1","related_material":{"record":[{"id":"10598","relation":"earlier_version","status":"public"}]}},{"day":"22","title":"FLEA: Provably robust fair multisource learning from unreliable training data","publication":"Transactions on Machine Learning Research","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"corr_author":"1","related_material":{"link":[{"url":"https://github.com/ISTAustria-CVML/FLEA","description":"source code","relation":"software"}]},"publication_identifier":{"issn":["2835-8856"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_published":"2022-12-22T00:00:00Z","oa_version":"Published Version","_id":"12495","arxiv":1,"year":"2022","article_type":"original","file":[{"content_type":"application/pdf","access_level":"open_access","checksum":"97c8a8470759cab597abb973ca137a3b","creator":"dernst","file_size":1948063,"relation":"main_file","file_name":"2022_TMLR_Iofinova.pdf","file_id":"12673","date_updated":"2023-02-23T10:30:04Z","date_created":"2023-02-23T10:30:04Z","success":1}],"type":"journal_article","acknowledgement":"The authors would like to thank Bernd Prach, Elias Frantar, Alexandra Peste, Mahdi Nikdan, and Peter Súkeník for their helpful feedback. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). This publication was made possible by an ETH AI Center postdoctoral fellowship granted to Nikola Konstantinov. Eugenia Iofinova was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. ","language":[{"iso":"eng"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"date_created":"2023-02-02T20:29:57Z","status":"public","quality_controlled":"1","external_id":{"arxiv":["2106.11732"]},"project":[{"_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","grant_number":"W1260-N35","name":"Vienna Graduate School on Computational Optimization"}],"month":"12","publication_status":"published","date_updated":"2025-12-30T11:04:31Z","abstract":[{"lang":"eng","text":"Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of\r\nmachine learning with far-reaching societal impact. However, existing fair learning methods\r\nare vulnerable to accidental or malicious artifacts in the training data, which can cause\r\nthem to unknowingly produce unfair classifiers. In this work we address the problem of\r\nfair learning from unreliable training data in the robust multisource setting, where the\r\navailable training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm\r\nthat identifies and suppresses those data sources that would have a negative impact on\r\nfairness or accuracy if they were used for training. As such, FLEA is not a replacement of\r\nprior fairness-aware learning methods but rather an augmentation that makes any of them\r\nrobust against unreliable training data. We show the effectiveness of our approach by a\r\ndiverse range of experiments on multiple datasets. Additionally, we prove formally that\r\n–given enough data– FLEA protects the learner against corruptions as long as the fraction of\r\naffected data sources is less than half. Our source code and documentation are available at\r\nhttps://github.com/ISTAustria-CVML/FLEA."}],"article_processing_charge":"No","main_file_link":[{"url":"https://openreview.net/forum?id=XsPopigZXV","open_access":"1"}],"file_date_updated":"2023-02-23T10:30:04Z","department":[{"_id":"ChLa"}],"publisher":"ML Research Press","has_accepted_license":"1","author":[{"full_name":"Iofinova, Eugenia B","first_name":"Eugenia B","last_name":"Iofinova","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","orcid":"0000-0002-7778-3221"},{"full_name":"Konstantinov, Nikola H","first_name":"Nikola H","last_name":"Konstantinov","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","orcid":"0009-0009-5204-7621"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","last_name":"Lampert","full_name":"Lampert, Christoph","first_name":"Christoph"}],"ddc":["000"],"citation":{"short":"E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022).","ista":"Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research.","mla":"Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” <i>Transactions on Machine Learning Research</i>, ML Research Press, 2022.","chicago":"Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” <i>Transactions on Machine Learning Research</i>. ML Research Press, 2022.","ieee":"E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust fair multisource learning from unreliable training data,” <i>Transactions on Machine Learning Research</i>. ML Research Press, 2022.","ama":"Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource learning from unreliable training data. <i>Transactions on Machine Learning Research</i>. 2022.","apa":"Iofinova, E. B., Konstantinov, N. H., &#38; Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. <i>Transactions on Machine Learning Research</i>. ML Research Press."}},{"file_date_updated":"2023-02-06T09:13:04Z","department":[{"_id":"ToHe"}],"abstract":[{"text":"A graph game is a two-player zero-sum game in which the players move a token throughout a graph to produce an infinite path, which determines the winner or payoff of the game. In bidding games, both players have budgets, and in each turn, we hold an \"auction\" (bidding) to determine which player moves the token. In this survey, we consider several bidding mechanisms and their effect on the properties of the game. Specifically, bidding games, and in particular bidding games of infinite duration, have an intriguing equivalence with random-turn games in which in each turn, the player who moves is chosen randomly. We summarize how minor changes in the bidding mechanism lead to unexpected differences in the equivalence with random-turn games.","lang":"eng"}],"article_processing_charge":"No","has_accepted_license":"1","publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","intvolume":"       241","doi":"10.4230/LIPIcs.MFCS.2022.3","author":[{"first_name":"Guy","full_name":"Avni, Guy","last_name":"Avni","orcid":"0000-0001-5588-8287","id":"463C8BC2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Henzinger, Thomas A","first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","last_name":"Henzinger"}],"ddc":["000"],"citation":{"mla":"Avni, Guy, and Thomas A. Henzinger. “An Updated Survey of Bidding Games on Graphs.” <i>47th International Symposium on Mathematical Foundations of Computer Science</i>, vol. 241, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, p. 3:1-3:6, doi:<a href=\"https://doi.org/10.4230/LIPIcs.MFCS.2022.3\">10.4230/LIPIcs.MFCS.2022.3</a>.","chicago":"Avni, Guy, and Thomas A Henzinger. “An Updated Survey of Bidding Games on Graphs.” In <i>47th International Symposium on Mathematical Foundations of Computer Science</i>, 241:3:1-3:6. Leibniz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. <a href=\"https://doi.org/10.4230/LIPIcs.MFCS.2022.3\">https://doi.org/10.4230/LIPIcs.MFCS.2022.3</a>.","ieee":"G. Avni and T. A. Henzinger, “An updated survey of bidding games on graphs,” in <i>47th International Symposium on Mathematical Foundations of Computer Science</i>, Vienna, Austria, 2022, vol. 241, p. 3:1-3:6.","apa":"Avni, G., &#38; Henzinger, T. A. (2022). An updated survey of bidding games on graphs. In <i>47th International Symposium on Mathematical Foundations of Computer Science</i> (Vol. 241, p. 3:1-3:6). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.MFCS.2022.3\">https://doi.org/10.4230/LIPIcs.MFCS.2022.3</a>","ama":"Avni G, Henzinger TA. An updated survey of bidding games on graphs. In: <i>47th International Symposium on Mathematical Foundations of Computer Science</i>. Vol 241. Leibniz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022:3:1-3:6. doi:<a href=\"https://doi.org/10.4230/LIPIcs.MFCS.2022.3\">10.4230/LIPIcs.MFCS.2022.3</a>","ista":"Avni G, Henzinger TA. 2022. An updated survey of bidding games on graphs. 47th International Symposium on Mathematical Foundations of Computer Science. MFCS: Mathematical Foundations of Computer ScienceLeibniz International Proceedings in Informatics (LIPIcs) vol. 241, 3:1-3:6.","short":"G. Avni, T.A. Henzinger, in:, 47th International Symposium on Mathematical Foundations of Computer Science, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 2022, p. 3:1-3:6."},"status":"public","date_created":"2023-02-05T17:26:01Z","place":"Dagstuhl, Germany","conference":{"location":"Vienna, Austria","name":"MFCS: Mathematical Foundations of Computer Science","start_date":"2022-08-22","end_date":"2022-08-26"},"quality_controlled":"1","ec_funded":1,"month":"08","project":[{"name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","call_identifier":"H2020"}],"date_updated":"2025-07-10T11:50:27Z","publication_status":"published","_id":"12509","oa_version":"Published Version","year":"2022","series_title":"Leibniz International Proceedings in Informatics (LIPIcs)","volume":241,"file":[{"date_created":"2023-02-06T09:13:04Z","success":1,"access_level":"open_access","checksum":"1888ec9421622f9526fbec2de035f132","content_type":"application/pdf","file_size":624586,"creator":"dernst","relation":"main_file","file_name":"2022_LIPICs_Avni.pdf","date_updated":"2023-02-06T09:13:04Z","file_id":"12519"}],"type":"conference","language":[{"iso":"eng"}],"acknowledgement":"Guy Avni: Work partially supported by the Israel Science Foundation, ISF grant agreement\r\nno 1679/21.\r\nThomas A. Henzinger: This work was supported in part by the ERC-2020-AdG 101020093.\r\nWe would like to thank all our collaborators Milad Aghajohari, Ventsislav Chonev, Rasmus Ibsen-Jensen, Ismäel Jecker, Petr Novotný, Josef Tkadlec, and Ðorđe Žikelić; we hope the collaboration was as fun and meaningful for you as it was for us.","title":"An updated survey of bidding games on graphs","day":"22","publication":"47th International Symposium on Mathematical Foundations of Computer Science","publication_identifier":{"isbn":["9783959772563"],"issn":["1868-8969"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"page":"3:1-3:6","corr_author":"1","fulldoi":"https://doi.org/10.4230/LIPIcs.MFCS.2022.3","date_published":"2022-08-22T00:00:00Z","oa":1},{"year":"2022","arxiv":1,"oa_version":"Preprint","_id":"12510","keyword":["General Medicine"],"article_type":"original","volume":36,"type":"journal_article","language":[{"iso":"eng"}],"acknowledgement":"SG is funded by the Austrian Science Fund (FWF) project number W1255-N23. ML and TH are supported in part by FWF under grant Z211-N23 (Wittgenstein Award) and the ERC-2020-AdG 101020093. SS is supported by NSF awards DCL-2040599, CCF-1918225, and CPS-1446832. RH and DR are partially supported by Boeing. RG is partially supported by Horizon-2020 ECSEL Project grant No. 783163 (iDev40).","title":"GoTube: Scalable statistical verification of continuous-depth models","day":"28","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","scopus_import":"1","publication_identifier":{"eissn":["2374-3468"],"isbn":["978577358350"],"issn":["2159-5399"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"6755-6764","date_published":"2022-06-28T00:00:00Z","fulldoi":"https://doi.org/10.1609/aaai.v36i6.20631","oa":1,"department":[{"_id":"ToHe"}],"abstract":[{"lang":"eng","text":"We introduce a new statistical verification algorithm that formally quantifies the behavioral robustness of any time-continuous process formulated as a continuous-depth model. Our algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states. We call our algorithm GoTube. Through its construction, GoTube ensures that the bounding tube is conservative up to a desired probability and up to a desired tightness.\r\n GoTube is implemented in JAX and optimized to scale to complex continuous-depth neural network models. Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube does not accumulate overapproximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques. We show that GoTube substantially outperforms state-of-the-art verification tools in terms of the size of the initial ball, speed, time-horizon, task completion, and scalability on a large set of experiments.\r\n GoTube is stable and sets the state-of-the-art in terms of its ability to scale to time horizons well beyond what has been previously possible."}],"article_processing_charge":"No","main_file_link":[{"url":"https://arxiv.org/abs/2107.08467","open_access":"1"}],"publisher":"Association for the Advancement of Artificial Intelligence","doi":"10.1609/aaai.v36i6.20631","intvolume":"        36","author":[{"full_name":"Gruenbacher, Sophie A.","first_name":"Sophie A.","last_name":"Gruenbacher"},{"last_name":"Lechner","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias","full_name":"Lechner, Mathias"},{"full_name":"Hasani, Ramin","first_name":"Ramin","last_name":"Hasani"},{"first_name":"Daniela","full_name":"Rus, Daniela","last_name":"Rus"},{"last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","first_name":"Thomas A"},{"full_name":"Smolka, Scott A.","first_name":"Scott A.","last_name":"Smolka"},{"last_name":"Grosu","first_name":"Radu","full_name":"Grosu, Radu"}],"citation":{"ista":"Gruenbacher SA, Lechner M, Hasani R, Rus D, Henzinger TA, Smolka SA, Grosu R. 2022. GoTube: Scalable statistical verification of continuous-depth models. Proceedings of the AAAI Conference on Artificial Intelligence. 36(6), 6755–6764.","short":"S.A. Gruenbacher, M. Lechner, R. Hasani, D. Rus, T.A. Henzinger, S.A. Smolka, R. Grosu, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 6755–6764.","chicago":"Gruenbacher, Sophie A., Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A Henzinger, Scott A. Smolka, and Radu Grosu. “GoTube: Scalable Statistical Verification of Continuous-Depth Models.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Association for the Advancement of Artificial Intelligence, 2022. <a href=\"https://doi.org/10.1609/aaai.v36i6.20631\">https://doi.org/10.1609/aaai.v36i6.20631</a>.","apa":"Gruenbacher, S. A., Lechner, M., Hasani, R., Rus, D., Henzinger, T. A., Smolka, S. A., &#38; Grosu, R. (2022). GoTube: Scalable statistical verification of continuous-depth models. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Association for the Advancement of Artificial Intelligence. <a href=\"https://doi.org/10.1609/aaai.v36i6.20631\">https://doi.org/10.1609/aaai.v36i6.20631</a>","ama":"Gruenbacher SA, Lechner M, Hasani R, et al. GoTube: Scalable statistical verification of continuous-depth models. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. 2022;36(6):6755-6764. doi:<a href=\"https://doi.org/10.1609/aaai.v36i6.20631\">10.1609/aaai.v36i6.20631</a>","ieee":"S. A. Gruenbacher <i>et al.</i>, “GoTube: Scalable statistical verification of continuous-depth models,” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 36, no. 6. Association for the Advancement of Artificial Intelligence, pp. 6755–6764, 2022.","mla":"Gruenbacher, Sophie A., et al. “GoTube: Scalable Statistical Verification of Continuous-Depth Models.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 36, no. 6, Association for the Advancement of Artificial Intelligence, 2022, pp. 6755–64, doi:<a href=\"https://doi.org/10.1609/aaai.v36i6.20631\">10.1609/aaai.v36i6.20631</a>."},"status":"public","date_created":"2023-02-05T17:27:42Z","external_id":{"arxiv":["2107.08467"]},"quality_controlled":"1","month":"06","ec_funded":1,"project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"Formal methods for the design and analysis of complex systems","grant_number":"Z211","call_identifier":"FWF"},{"_id":"62781420-2b32-11ec-9570-8d9b63373d4d","name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","call_identifier":"H2020"}],"date_updated":"2025-04-15T06:26:14Z","issue":"6","publication_status":"published"},{"department":[{"_id":"ToHe"},{"_id":"KrCh"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2112.09495"}],"abstract":[{"lang":"eng","text":"We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control systems is extensively studied in the literature, verifying stability in stochastic control systems is an open problem. The few existing works on this topic either consider only specialized forms of stochasticity or make restrictive assumptions on the system, rendering them inapplicable to learning algorithms with neural network policies. \r\n In this work, we present an approach for general nonlinear stochastic control problems with two novel aspects: (a) instead of classical stochastic extensions of Lyapunov functions, we use ranking supermartingales (RSMs) to certify a.s. asymptotic stability, and (b) we present a method for learning neural network RSMs. \r\n We prove that our approach guarantees a.s. asymptotic stability of the system and\r\n provides the first method to obtain bounds on the stabilization time, which stochastic Lyapunov functions do not.\r\n Finally, we validate our approach experimentally on a set of nonlinear stochastic reinforcement learning environments with neural network policies."}],"article_processing_charge":"No","publisher":"Association for the Advancement of Artificial Intelligence","intvolume":"        36","doi":"10.1609/aaai.v36i7.20695","author":[{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","last_name":"Lechner","first_name":"Mathias","full_name":"Lechner, Mathias"},{"last_name":"Zikelic","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4681-1699","full_name":"Zikelic, Dorde","first_name":"Dorde"},{"full_name":"Chatterjee, Krishnendu","first_name":"Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X","last_name":"Chatterjee"},{"last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","first_name":"Thomas A"}],"citation":{"short":"M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.","ista":"Lechner M, Zikelic D, Chatterjee K, Henzinger TA. 2022. Stability verification in stochastic control systems via neural network supermartingales. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7), 7326–7336.","chicago":"Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, and Thomas A Henzinger. “Stability Verification in Stochastic Control Systems via Neural Network Supermartingales.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Association for the Advancement of Artificial Intelligence, 2022. <a href=\"https://doi.org/10.1609/aaai.v36i7.20695\">https://doi.org/10.1609/aaai.v36i7.20695</a>.","apa":"Lechner, M., Zikelic, D., Chatterjee, K., &#38; Henzinger, T. A. (2022). Stability verification in stochastic control systems via neural network supermartingales. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Association for the Advancement of Artificial Intelligence. <a href=\"https://doi.org/10.1609/aaai.v36i7.20695\">https://doi.org/10.1609/aaai.v36i7.20695</a>","ieee":"M. Lechner, D. Zikelic, K. Chatterjee, and T. A. Henzinger, “Stability verification in stochastic control systems via neural network supermartingales,” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 36, no. 7. Association for the Advancement of Artificial Intelligence, pp. 7326–7336, 2022.","ama":"Lechner M, Zikelic D, Chatterjee K, Henzinger TA. Stability verification in stochastic control systems via neural network supermartingales. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. 2022;36(7):7326-7336. doi:<a href=\"https://doi.org/10.1609/aaai.v36i7.20695\">10.1609/aaai.v36i7.20695</a>","mla":"Lechner, Mathias, et al. “Stability Verification in Stochastic Control Systems via Neural Network Supermartingales.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 36, no. 7, Association for the Advancement of Artificial Intelligence, 2022, pp. 7326–36, doi:<a href=\"https://doi.org/10.1609/aaai.v36i7.20695\">10.1609/aaai.v36i7.20695</a>."},"status":"public","date_created":"2023-02-05T17:29:50Z","external_id":{"arxiv":["2112.09495"]},"quality_controlled":"1","ec_funded":1,"month":"06","project":[{"call_identifier":"H2020","name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"},{"_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818","call_identifier":"H2020"},{"grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"issue":"7","date_updated":"2026-04-07T13:27:55Z","publication_status":"published","_id":"12511","oa_version":"Preprint","year":"2022","arxiv":1,"keyword":["General Medicine"],"article_type":"original","volume":36,"type":"journal_article","language":[{"iso":"eng"}],"acknowledgement":"This work was supported in part by the ERC-2020-AdG 101020093, ERC CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation programme\r\nunder the Marie Skłodowska-Curie Grant Agreement No. 665385.","title":"Stability verification in stochastic control systems via neural network supermartingales","day":"28","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","publication_identifier":{"issn":["2159-5399"],"eissn":["2374-3468"],"isbn":["9781577358350"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"14539"}]},"page":"7326-7336","corr_author":"1","fulldoi":"https://doi.org/10.1609/aaai.v36i7.20695","date_published":"2022-06-28T00:00:00Z","oa":1},{"publisher":"Springer Nature","department":[{"_id":"KrPi"}],"main_file_link":[{"url":"https://eprint.iacr.org/2022/093","open_access":"1"}],"abstract":[{"text":"The homogeneous continuous LWE (hCLWE) problem is to distinguish samples of a specific high-dimensional Gaussian mixture from standard normal samples. It was shown to be at least as hard as Learning with Errors, but no reduction in the other direction is currently known.\r\nWe present four new public-key encryption schemes based on the hardness of hCLWE, with varying tradeoffs between decryption and security errors, and different discretization techniques. Our schemes yield a polynomial-time algorithm for solving hCLWE using a Statistical Zero-Knowledge oracle.","lang":"eng"}],"article_processing_charge":"No","citation":{"short":"A. Bogdanov, M. Cueto Noval, C. Hoffmann, A. Rosen, in:, Theory of Cryptography, Springer Nature, 2022, pp. 565–592.","ista":"Bogdanov A, Cueto Noval M, Hoffmann C, Rosen A. 2022. Public-Key Encryption from Homogeneous CLWE. Theory of Cryptography. TCC: Theory of Cryptography, LNCS, vol. 13748, 565–592.","mla":"Bogdanov, Andrej, et al. “Public-Key Encryption from Homogeneous CLWE.” <i>Theory of Cryptography</i>, vol. 13748, Springer Nature, 2022, pp. 565–92, doi:<a href=\"https://doi.org/10.1007/978-3-031-22365-5_20\">10.1007/978-3-031-22365-5_20</a>.","ieee":"A. Bogdanov, M. Cueto Noval, C. Hoffmann, and A. Rosen, “Public-Key Encryption from Homogeneous CLWE,” in <i>Theory of Cryptography</i>, Chicago, IL, United States, 2022, vol. 13748, pp. 565–592.","ama":"Bogdanov A, Cueto Noval M, Hoffmann C, Rosen A. Public-Key Encryption from Homogeneous CLWE. In: <i>Theory of Cryptography</i>. Vol 13748. Springer Nature; 2022:565-592. doi:<a href=\"https://doi.org/10.1007/978-3-031-22365-5_20\">10.1007/978-3-031-22365-5_20</a>","apa":"Bogdanov, A., Cueto Noval, M., Hoffmann, C., &#38; Rosen, A. (2022). Public-Key Encryption from Homogeneous CLWE. In <i>Theory of Cryptography</i> (Vol. 13748, pp. 565–592). Chicago, IL, United States: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-031-22365-5_20\">https://doi.org/10.1007/978-3-031-22365-5_20</a>","chicago":"Bogdanov, Andrej, Miguel Cueto Noval, Charlotte Hoffmann, and Alon Rosen. “Public-Key Encryption from Homogeneous CLWE.” In <i>Theory of Cryptography</i>, 13748:565–92. Springer Nature, 2022. <a href=\"https://doi.org/10.1007/978-3-031-22365-5_20\">https://doi.org/10.1007/978-3-031-22365-5_20</a>."},"intvolume":"     13748","doi":"10.1007/978-3-031-22365-5_20","author":[{"first_name":"Andrej","full_name":"Bogdanov, Andrej","last_name":"Bogdanov"},{"first_name":"Miguel","full_name":"Cueto Noval, Miguel","orcid":"0000-0002-2505-4246","id":"ffc563a3-f6e0-11ea-865d-e3cce03d17cc","last_name":"Cueto Noval"},{"full_name":"Hoffmann, Charlotte","first_name":"Charlotte","last_name":"Hoffmann","id":"0f78d746-dc7d-11ea-9b2f-83f92091afe7","orcid":"0000-0003-2027-5549"},{"last_name":"Rosen","full_name":"Rosen, Alon","first_name":"Alon"}],"conference":{"start_date":"2022-11-07","name":"TCC: Theory of Cryptography","end_date":"2022-11-10","location":"Chicago, IL, United States"},"external_id":{"isi":["000921318200020"]},"quality_controlled":"1","status":"public","date_created":"2023-02-05T23:01:00Z","date_updated":"2024-10-09T21:04:05Z","publication_status":"published","month":"12","_id":"12516","oa_version":"Preprint","year":"2022","language":[{"iso":"eng"}],"acknowledgement":"We are grateful to Devika Sharma and Luca Trevisan for their insight and advice and to an anonymous reviewer for helpful comments.\r\n\r\nThis work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101019547). The first author was additionally supported by RGC GRF CUHK14209920 and the fourth author was additionally supported by ISF grant No. 1399/17, project PROMETHEUS (Grant 780701), and Cariplo CRYPTONOMEX grant.","volume":13748,"type":"conference","publication":"Theory of Cryptography","title":"Public-Key Encryption from Homogeneous CLWE","alternative_title":["LNCS"],"day":"21","fulldoi":"https://doi.org/10.1007/978-3-031-22365-5_20","date_published":"2022-12-21T00:00:00Z","oa":1,"isi":1,"scopus_import":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication_identifier":{"isbn":["9783031223648"],"eissn":["1611-3349"],"issn":["0302-9743"]},"page":"565-592","corr_author":"1"},{"month":"09","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"status":"public","id":"12118","relation":"used_in_publication"},{"status":"public","relation":"used_in_publication","id":"13286"}]},"corr_author":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"date_published":"2022-09-25T00:00:00Z","date_updated":"2026-04-07T13:27:22Z","fulldoi":"https://doi.org/10.15479/AT:ISTA:12102","oa":1,"title":"Data for \"Majorana-like Coulomb spectroscopy in the absence of zero bias peaks\"","status":"public","day":"25","contributor":[{"first_name":"Marco","last_name":"Valentini","contributor_type":"contact_person","id":"C0BB2FAC-D767-11E9-B658-BC13E6697425"}],"date_created":"2023-02-07T08:13:39Z","doi":"10.15479/AT:ISTA:12102","type":"research_data","file":[{"date_created":"2023-02-07T08:18:24Z","success":1,"creator":"dernst","file_size":3609122411,"content_type":"application/x-zip-compressed","checksum":"0dbd6327bf84c7e81b295c4bc9d12826","access_level":"open_access","file_id":"12523","date_updated":"2023-02-07T08:18:24Z","file_name":"Majorana_like.zip","relation":"main_file"}],"author":[{"full_name":"Valentini, Marco","first_name":"Marco","id":"C0BB2FAC-D767-11E9-B658-BC13E6697425","last_name":"Valentini"},{"first_name":"Pablo","full_name":"San-Jose, Pablo","last_name":"San-Jose"},{"first_name":"Jordi","full_name":"Arbiol, Jordi","last_name":"Arbiol"},{"first_name":"Sara","full_name":"Marti-Sanchez, Sara","last_name":"Marti-Sanchez"},{"full_name":"Botifoll, Marc","first_name":"Marc","last_name":"Botifoll"}],"citation":{"mla":"Valentini, Marco, et al. <i>Data for “Majorana-like Coulomb Spectroscopy in the Absence of Zero Bias Peaks.”</i> Institute of Science and Technology Austria, 2022, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:12102\">10.15479/AT:ISTA:12102</a>.","ieee":"M. Valentini, P. San-Jose, J. Arbiol, S. Marti-Sanchez, and M. Botifoll, “Data for ‘Majorana-like Coulomb spectroscopy in the absence of zero bias peaks.’” Institute of Science and Technology Austria, 2022.","apa":"Valentini, M., San-Jose, P., Arbiol, J., Marti-Sanchez, S., &#38; Botifoll, M. (2022). Data for “Majorana-like Coulomb spectroscopy in the absence of zero bias peaks.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:12102\">https://doi.org/10.15479/AT:ISTA:12102</a>","ama":"Valentini M, San-Jose P, Arbiol J, Marti-Sanchez S, Botifoll M. Data for “Majorana-like Coulomb spectroscopy in the absence of zero bias peaks.” 2022. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:12102\">10.15479/AT:ISTA:12102</a>","chicago":"Valentini, Marco, Pablo San-Jose, Jordi Arbiol, Sara Marti-Sanchez, and Marc Botifoll. “Data for ‘Majorana-like Coulomb Spectroscopy in the Absence of Zero Bias Peaks.’” Institute of Science and Technology Austria, 2022. <a href=\"https://doi.org/10.15479/AT:ISTA:12102\">https://doi.org/10.15479/AT:ISTA:12102</a>.","short":"M. Valentini, P. San-Jose, J. Arbiol, S. Marti-Sanchez, M. Botifoll, (2022).","ista":"Valentini M, San-Jose P, Arbiol J, Marti-Sanchez S, Botifoll M. 2022. Data for ‘Majorana-like Coulomb spectroscopy in the absence of zero bias peaks’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:12102\">10.15479/AT:ISTA:12102</a>."},"ddc":["530"],"department":[{"_id":"GeKa"}],"file_date_updated":"2023-02-07T08:18:24Z","year":"2022","_id":"12522","oa_version":"Published Version","abstract":[{"text":"This .zip File contains the transport data, the codes for the data analysis, the microscopy analysis and the codes for the theoretical simulations for \"Majorana-like Coulomb spectroscopy in the absence of zero bias peaks\" by M. Valentini, et. al. The transport data are saved with hdf5 file format. The files can be open with the log browser of Labber.","lang":"eng"}],"article_processing_charge":"No","publisher":"Institute of Science and Technology Austria","has_accepted_license":"1"},{"doi":"10.1109/ITW54588.2022.9965870","author":[{"last_name":"Amani","full_name":"Amani, Mohammad Hossein","first_name":"Mohammad Hossein"},{"id":"ca726dda-de17-11ea-bc14-f9da834f63aa","last_name":"Bombari","full_name":"Bombari, Simone","first_name":"Simone"},{"last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","orcid":"0000-0002-3242-7020","full_name":"Mondelli, Marco","first_name":"Marco"},{"last_name":"Pukdee","first_name":"Rattana","full_name":"Pukdee, Rattana"},{"full_name":"Rini, Stefano","first_name":"Stefano","last_name":"Rini"}],"citation":{"ista":"Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. 2022. Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop., 588–593.","short":"M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information Theory Workshop (2022) 588–593.","mla":"Amani, Mohammad Hossein, et al. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.” <i>IEEE Information Theory Workshop</i>, IEEE, 2022, pp. 588–93, doi:<a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">10.1109/ITW54588.2022.9965870</a>.","chicago":"Amani, Mohammad Hossein, Simone Bombari, Marco Mondelli, Rattana Pukdee, and Stefano Rini. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.” <i>IEEE Information Theory Workshop</i>. IEEE, 2022. <a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">https://doi.org/10.1109/ITW54588.2022.9965870</a>.","ieee":"M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, “Sharp asymptotics on the compression of two-layer neural networks,” <i>IEEE Information Theory Workshop</i>. IEEE, pp. 588–593, 2022.","apa":"Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., &#38; Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. <i>IEEE Information Theory Workshop</i>. Mumbai, India: IEEE. <a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">https://doi.org/10.1109/ITW54588.2022.9965870</a>","ama":"Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. Sharp asymptotics on the compression of two-layer neural networks. <i>IEEE Information Theory Workshop</i>. 2022:588-593. doi:<a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">10.1109/ITW54588.2022.9965870</a>"},"department":[{"_id":"MaMo"}],"abstract":[{"text":"In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimize the population L_2 loss between the outputs of the target and of the compressed network, under the assumption of Gaussian inputs. By using tools from high-dimensional probability, we show that this non-convex problem can be simplified when the target network is sufficiently over-parameterized, and provide the error rate of this approximation as a function of the input dimension and N. In this mean-field limit, the simplified objective, as well as the optimal weights of the compressed network, does not depend on the realization of the target network, but only on expected scaling factors. Furthermore, for networks with ReLU activation, we conjecture that the optimum of the simplified optimization problem is achieved by taking weights on the Equiangular Tight Frame (ETF), while the scaling of the weights and the orientation of the ETF depend on the parameters of the target network. Numerical evidence is provided to support this conjecture.","lang":"eng"}],"article_processing_charge":"No","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2205.08199","open_access":"1"}],"publisher":"IEEE","month":"11","date_updated":"2025-09-10T09:53:31Z","publication_status":"published","status":"public","date_created":"2023-02-10T13:47:56Z","external_id":{"arxiv":["2205.08199"],"isi":["000904341100099"]},"conference":{"name":"ITW: Information Theory Workshop","start_date":"2022-11-01","end_date":"2022-11-09","location":"Mumbai, India"},"quality_controlled":"1","type":"journal_article","language":[{"iso":"eng"}],"year":"2022","arxiv":1,"oa_version":"Preprint","_id":"12538","article_type":"original","scopus_import":"1","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publication_identifier":{"isbn":["9781665483414"]},"page":"588-593","date_published":"2022-11-16T00:00:00Z","fulldoi":"https://doi.org/10.1109/ITW54588.2022.9965870","isi":1,"oa":1,"title":"Sharp asymptotics on the compression of two-layer neural networks","day":"16","publication":"IEEE Information Theory Workshop"},{"day":"28","title":"Risk-aware stochastic shortest path","publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022","page":"9858-9867","corr_author":"1","publication_identifier":{"isbn":["1577358767"],"eissn":["2374-3468"]},"scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_published":"2022-06-28T00:00:00Z","fulldoi":"https://doi.org/10.1609/aaai.v36i9.21222","arxiv":1,"year":"2022","oa_version":"Preprint","_id":"12568","type":"conference","volume":36,"language":[{"iso":"eng"}],"date_created":"2023-02-19T23:00:56Z","status":"public","quality_controlled":"1","external_id":{"arxiv":["2203.01640"]},"conference":{"location":"Virtual","name":"Conference on Artificial Intelligence","start_date":"2022-02-22","end_date":"2022-03-01"},"month":"06","publication_status":"published","date_updated":"2024-10-09T21:04:32Z","issue":"9","abstract":[{"lang":"eng","text":"We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models."}],"article_processing_charge":"No","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2203.01640"}],"department":[{"_id":"KrCh"}],"publisher":"Association for the Advancement of Artificial Intelligence","author":[{"first_name":"Tobias","full_name":"Meggendorfer, Tobias","orcid":"0000-0002-1712-2165","id":"b21b0c15-30a2-11eb-80dc-f13ca25802e1","last_name":"Meggendorfer"}],"doi":"10.1609/aaai.v36i9.21222","intvolume":"        36","citation":{"ista":"Meggendorfer T. 2022. Risk-aware stochastic shortest path. Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Conference on Artificial Intelligence vol. 36, 9858–9867.","short":"T. Meggendorfer, in:, Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–9867.","apa":"Meggendorfer, T. (2022). Risk-aware stochastic shortest path. In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i> (Vol. 36, pp. 9858–9867). Virtual: Association for the Advancement of Artificial Intelligence. <a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">https://doi.org/10.1609/aaai.v36i9.21222</a>","ieee":"T. Meggendorfer, “Risk-aware stochastic shortest path,” in <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>, Virtual, 2022, vol. 36, no. 9, pp. 9858–9867.","ama":"Meggendorfer T. Risk-aware stochastic shortest path. In: <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>. Vol 36. Association for the Advancement of Artificial Intelligence; 2022:9858-9867. doi:<a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">10.1609/aaai.v36i9.21222</a>","chicago":"Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>, 36:9858–67. Association for the Advancement of Artificial Intelligence, 2022. <a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">https://doi.org/10.1609/aaai.v36i9.21222</a>.","mla":"Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>, vol. 36, no. 9, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–67, doi:<a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">10.1609/aaai.v36i9.21222</a>."}},{"publication":"Journal of Integrative Plant Biology","day":"07","title":"DNA methylation dynamics during germline development","oa":1,"date_published":"2022-12-07T00:00:00Z","fulldoi":"https://doi.org/10.1111/jipb.13422","page":"2240-2251","publication_identifier":{"eissn":["1744-7909"],"issn":["1672-9072"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","article_type":"review","keyword":["Plant Science","General Biochemistry","Genetics and Molecular Biology","Biochemistry"],"year":"2022","oa_version":"Published Version","_id":"12670","pmid":1,"language":[{"iso":"eng"}],"type":"journal_article","volume":64,"quality_controlled":"1","external_id":{"pmid":["36478632"]},"date_created":"2023-02-23T09:15:57Z","status":"public","publication_status":"published","date_updated":"2024-10-14T12:03:14Z","issue":"12","month":"12","publisher":"Wiley","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1111/jipb.13422"}],"article_processing_charge":"No","abstract":[{"lang":"eng","text":"DNA methylation plays essential homeostatic functions in eukaryotic genomes. In animals, DNA methylation is also developmentally regulated and, in turn, regulates development. In the past two decades, huge research effort has endorsed the understanding that DNA methylation plays a similar role in plant development, especially during sexual reproduction. The power of whole-genome sequencing and cell isolation techniques, as well as bioinformatics tools, have enabled recent studies to reveal dynamic changes in DNA methylation during germline development. Furthermore, the combination of these technological advances with genetics, developmental biology and cell biology tools has revealed functional methylation reprogramming events that control gene and transposon activities in flowering plant germlines. In this review, we discuss the major advances in our knowledge of DNA methylation dynamics during male and female germline development in flowering plants."}],"extern":"1","department":[{"_id":"XiFe"}],"citation":{"short":"S. He, X. Feng, Journal of Integrative Plant Biology 64 (2022) 2240–2251.","ista":"He S, Feng X. 2022. DNA methylation dynamics during germline development. Journal of Integrative Plant Biology. 64(12), 2240–2251.","mla":"He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.” <i>Journal of Integrative Plant Biology</i>, vol. 64, no. 12, Wiley, 2022, pp. 2240–51, doi:<a href=\"https://doi.org/10.1111/jipb.13422\">10.1111/jipb.13422</a>.","chicago":"He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.” <i>Journal of Integrative Plant Biology</i>. Wiley, 2022. <a href=\"https://doi.org/10.1111/jipb.13422\">https://doi.org/10.1111/jipb.13422</a>.","ieee":"S. He and X. Feng, “DNA methylation dynamics during germline development,” <i>Journal of Integrative Plant Biology</i>, vol. 64, no. 12. Wiley, pp. 2240–2251, 2022.","ama":"He S, Feng X. DNA methylation dynamics during germline development. <i>Journal of Integrative Plant Biology</i>. 2022;64(12):2240-2251. doi:<a href=\"https://doi.org/10.1111/jipb.13422\">10.1111/jipb.13422</a>","apa":"He, S., &#38; Feng, X. (2022). DNA methylation dynamics during germline development. <i>Journal of Integrative Plant Biology</i>. Wiley. <a href=\"https://doi.org/10.1111/jipb.13422\">https://doi.org/10.1111/jipb.13422</a>"},"author":[{"last_name":"He","first_name":"Shengbo","full_name":"He, Shengbo"},{"orcid":"0000-0002-4008-1234","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","last_name":"Feng","first_name":"Xiaoqi","full_name":"Feng, Xiaoqi"}],"doi":"10.1111/jipb.13422","intvolume":"        64"},{"author":[{"last_name":"Buttress","full_name":"Buttress, Toby","first_name":"Toby"},{"last_name":"He","full_name":"He, Shengbo","first_name":"Shengbo"},{"first_name":"Liang","full_name":"Wang, Liang","last_name":"Wang"},{"first_name":"Shaoli","full_name":"Zhou, Shaoli","last_name":"Zhou"},{"full_name":"Saalbach, Gerhard","first_name":"Gerhard","last_name":"Saalbach"},{"first_name":"Martin","full_name":"Vickers, Martin","last_name":"Vickers"},{"full_name":"Li, Guohong","first_name":"Guohong","last_name":"Li"},{"first_name":"Pilong","full_name":"Li, Pilong","last_name":"Li"},{"full_name":"Feng, Xiaoqi","first_name":"Xiaoqi","last_name":"Feng","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","orcid":"0000-0002-4008-1234"}],"doi":"10.1038/s41586-022-05386-6","intvolume":"       611","citation":{"short":"T. Buttress, S. He, L. Wang, S. Zhou, G. Saalbach, M. Vickers, G. Li, P. Li, X. Feng, Nature 611 (2022) 614–622.","ista":"Buttress T, He S, Wang L, Zhou S, Saalbach G, Vickers M, Li G, Li P, Feng X. 2022. Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. Nature. 611(7936), 614–622.","mla":"Buttress, Toby, et al. “Histone H2B.8 Compacts Flowering Plant Sperm through Chromatin Phase Separation.” <i>Nature</i>, vol. 611, no. 7936, Springer Nature, 2022, pp. 614–22, doi:<a href=\"https://doi.org/10.1038/s41586-022-05386-6\">10.1038/s41586-022-05386-6</a>.","apa":"Buttress, T., He, S., Wang, L., Zhou, S., Saalbach, G., Vickers, M., … Feng, X. (2022). Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41586-022-05386-6\">https://doi.org/10.1038/s41586-022-05386-6</a>","ama":"Buttress T, He S, Wang L, et al. Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. <i>Nature</i>. 2022;611(7936):614-622. doi:<a href=\"https://doi.org/10.1038/s41586-022-05386-6\">10.1038/s41586-022-05386-6</a>","ieee":"T. Buttress <i>et al.</i>, “Histone H2B.8 compacts flowering plant sperm through chromatin phase separation,” <i>Nature</i>, vol. 611, no. 7936. Springer Nature, pp. 614–622, 2022.","chicago":"Buttress, Toby, Shengbo He, Liang Wang, Shaoli Zhou, Gerhard Saalbach, Martin Vickers, Guohong Li, Pilong Li, and Xiaoqi Feng. “Histone H2B.8 Compacts Flowering Plant Sperm through Chromatin Phase Separation.” <i>Nature</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1038/s41586-022-05386-6\">https://doi.org/10.1038/s41586-022-05386-6</a>."},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41586-022-05386-6"}],"article_processing_charge":"No","abstract":[{"lang":"eng","text":"Sperm chromatin is typically transformed by protamines into a compact and transcriptionally inactive state1,2. Sperm cells of flowering plants lack protamines, yet they have small, transcriptionally active nuclei with chromatin condensed through an unknown mechanism3,4. Here we show that a histone variant, H2B.8, mediates sperm chromatin and nuclear condensation in Arabidopsis thaliana. Loss of H2B.8 causes enlarged sperm nuclei with dispersed chromatin, whereas ectopic expression in somatic cells produces smaller nuclei with aggregated chromatin. This result demonstrates that H2B.8 is sufficient for chromatin condensation. H2B.8 aggregates transcriptionally inactive AT-rich chromatin into phase-separated condensates, which facilitates nuclear compaction without reducing transcription. Reciprocal crosses show that mutation of h2b.8 reduces male transmission, which suggests that H2B.8-mediated sperm compaction is important for fertility. Altogether, our results reveal a new mechanism of nuclear compaction through global aggregation of unexpressed chromatin. We propose that H2B.8 is an evolutionary innovation of flowering plants that achieves nuclear condensation compatible with active transcription."}],"department":[{"_id":"XiFe"}],"extern":"1","publisher":"Springer Nature","month":"11","publication_status":"published","date_updated":"2024-10-14T12:03:36Z","issue":"7936","date_created":"2023-02-23T09:17:05Z","status":"public","quality_controlled":"1","external_id":{"pmid":["36323776"]},"type":"journal_article","volume":611,"pmid":1,"language":[{"iso":"eng"}],"year":"2022","_id":"12671","oa_version":"Published Version","article_type":"original","page":"614-622","publication_identifier":{"issn":["0028-0836"],"eissn":["1476-4687"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","oa":1,"date_published":"2022-11-17T00:00:00Z","fulldoi":"https://doi.org/10.1038/s41586-022-05386-6","day":"17","title":"Histone H2B.8 compacts flowering plant sperm through chromatin phase separation","publication":"Nature"},{"article_number":"2209.14368","title":"Repeated prophet inequality with near-optimal bounds","day":"28","publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","corr_author":"1","fulldoi":"https://doi.org/10.48550/ARXIV.2209.14368","date_published":"2022-09-28T00:00:00Z","oa":1,"_id":"12677","oa_version":"Preprint","arxiv":1,"year":"2022","type":"preprint","language":[{"iso":"eng"}],"acknowledgement":"This research was partially supported by the ERC CoG 863818 (ForM-SMArt) grant.","status":"public","date_created":"2023-02-24T12:21:40Z","external_id":{"arxiv":["2209.14368"]},"month":"09","ec_funded":1,"project":[{"name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","call_identifier":"H2020"}],"date_updated":"2025-04-14T07:52:48Z","publication_status":"submitted","department":[{"_id":"GradSch"},{"_id":"KrCh"}],"main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2209.14368"}],"article_processing_charge":"No","abstract":[{"lang":"eng","text":"In modern sample-driven Prophet Inequality, an adversary chooses a sequence of n items with values v1,v2,…,vn to be presented to a decision maker (DM). The process follows in two phases. In the first phase (sampling phase), some items, possibly selected at random, are revealed to the DM, but she can never accept them. In the second phase, the DM is presented with the other items in a random order and online fashion. For each item, she must make an irrevocable decision to either accept the item and stop the process or reject the item forever and proceed to the next item. The goal of the DM is to maximize the expected value as compared to a Prophet (or offline algorithm) that has access to all information. In this setting, the sampling phase has no cost and is not part of the optimization process. However, in many scenarios, the samples are obtained as part of the decision-making process.\r\nWe model this aspect as a two-phase Prophet Inequality where an adversary chooses a sequence of 2n items with values v1,v2,…,v2n and the items are randomly ordered. Finally, there are two phases of the Prophet Inequality problem with the first n-items and the rest of the items, respectively. We show that some basic algorithms achieve a ratio of at most 0.450. We present an algorithm that achieves a ratio of at least 0.495. Finally, we show that for every algorithm the ratio it can achieve is at most 0.502. Hence our algorithm is near-optimal."}],"doi":"10.48550/ARXIV.2209.14368","author":[{"first_name":"Krishnendu","full_name":"Chatterjee, Krishnendu","last_name":"Chatterjee","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"},{"id":"4363614d-b686-11ed-a7d5-ac9e4a24bc2e","last_name":"Mohammadi","first_name":"Mona","full_name":"Mohammadi, Mona"},{"last_name":"Saona Urmeneta","id":"BD1DF4C4-D767-11E9-B658-BC13E6697425","orcid":"0000-0001-5103-038X","full_name":"Saona Urmeneta, Raimundo J","first_name":"Raimundo J"}],"citation":{"mla":"Chatterjee, Krishnendu, et al. “Repeated Prophet Inequality with Near-Optimal Bounds.” <i>ArXiv</i>, 2209.14368, doi:<a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">10.48550/ARXIV.2209.14368</a>.","chicago":"Chatterjee, Krishnendu, Mona Mohammadi, and Raimundo J Saona Urmeneta. “Repeated Prophet Inequality with Near-Optimal Bounds.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">https://doi.org/10.48550/ARXIV.2209.14368</a>.","ama":"Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with near-optimal bounds. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">10.48550/ARXIV.2209.14368</a>","apa":"Chatterjee, K., Mohammadi, M., &#38; Saona Urmeneta, R. J. (n.d.). Repeated prophet inequality with near-optimal bounds. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">https://doi.org/10.48550/ARXIV.2209.14368</a>","ieee":"K. Chatterjee, M. Mohammadi, and R. J. Saona Urmeneta, “Repeated prophet inequality with near-optimal bounds,” <i>arXiv</i>. .","short":"K. Chatterjee, M. Mohammadi, R.J. Saona Urmeneta, ArXiv (n.d.).","ista":"Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with near-optimal bounds. arXiv, 2209.14368."}},{"_id":"12750","oa_version":"Preprint","year":"2022","arxiv":1,"type":"preprint","language":[{"iso":"eng"}],"day":"07","article_number":"2210.15607","title":"Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models","publication":"arXiv","tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","image":"/images/cc_by_nc_sa.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","short":"CC BY-NC-SA (4.0)"},"corr_author":"1","related_material":{"record":[{"relation":"later_version","id":"14334","status":"public"},{"status":"public","relation":"dissertation_contains","id":"12732"}]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"fulldoi":"https://doi.org/10.48550/arXiv.2210.15607","date_published":"2022-11-07T00:00:00Z","abstract":[{"lang":"eng","text":"Quantum kinetically constrained models have recently attracted significant attention due to their anomalous dynamics and thermalization. In this work, we introduce a hitherto unexplored family of kinetically constrained models featuring a conserved particle number and strong inversion-symmetry breaking due to facilitated hopping. We demonstrate that these models provide a generic example of so-called quantum Hilbert space fragmentation, that is manifested in disconnected sectors in the Hilbert space that are not apparent in the computational basis. Quantum Hilbert space fragmentation leads to an exponential in system size number of eigenstates with exactly zero entanglement entropy across several bipartite cuts. These eigenstates can be probed dynamically using quenches from simple initial product states. In addition, we study the particle spreading under unitary dynamics launched from the domain wall state, and find faster than diffusive dynamics at high particle densities, that crosses over into logarithmically slow relaxation at smaller densities. Using a classically simulable cellular automaton, we reproduce the logarithmic dynamics observed in the quantum case. Our work suggests that particle conserving constrained models with inversion symmetry breaking realize so far unexplored universality classes of dynamics and invite their further theoretical and experimental studies."}],"article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2210.15607","open_access":"1"}],"license":"https://creativecommons.org/licenses/by-nc-sa/4.0/","department":[{"_id":"GradSch"},{"_id":"MaSe"}],"author":[{"orcid":"0000-0002-7969-2729","id":"4115AF5C-F248-11E8-B48F-1D18A9856A87","last_name":"Brighi","first_name":"Pietro","full_name":"Brighi, Pietro"},{"id":"F75EE9BE-5C90-11EA-905D-16643DDC885E","orcid":"0000-0003-0038-7068","last_name":"Ljubotina","full_name":"Ljubotina, Marko","first_name":"Marko"},{"id":"47809E7E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2399-5827","last_name":"Serbyn","full_name":"Serbyn, Maksym","first_name":"Maksym"}],"doi":"10.48550/arXiv.2210.15607","citation":{"mla":"Brighi, Pietro, et al. “Hilbert Space Fragmentation and Slow Dynamics in Particle-Conserving Quantum East Models.” <i>ArXiv</i>, 2210.15607, doi:<a href=\"https://doi.org/10.48550/arXiv.2210.15607\">10.48550/arXiv.2210.15607</a>.","chicago":"Brighi, Pietro, Marko Ljubotina, and Maksym Serbyn. “Hilbert Space Fragmentation and Slow Dynamics in Particle-Conserving Quantum East Models.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2210.15607\">https://doi.org/10.48550/arXiv.2210.15607</a>.","ieee":"P. Brighi, M. Ljubotina, and M. Serbyn, “Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models,” <i>arXiv</i>. .","apa":"Brighi, P., Ljubotina, M., &#38; Serbyn, M. (n.d.). Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2210.15607\">https://doi.org/10.48550/arXiv.2210.15607</a>","ama":"Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2210.15607\">10.48550/arXiv.2210.15607</a>","ista":"Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. arXiv, 2210.15607.","short":"P. Brighi, M. Ljubotina, M. Serbyn, ArXiv (n.d.)."},"date_created":"2023-03-23T14:33:13Z","OA_place":"repository","status":"public","external_id":{"arxiv":["2210.15607"]},"month":"11","publication_status":"draft","date_updated":"2026-04-07T13:26:31Z"},{"status":"public","date_created":"2023-03-28T09:21:09Z","quality_controlled":"1","month":"08","project":[{"call_identifier":"FWF","_id":"26AEDAB2-B435-11E9-9278-68D0E5697425","grant_number":"P32428","name":"New frontiers of the Manin conjecture"}],"date_updated":"2025-04-15T07:39:01Z","publication_status":"published","department":[{"_id":"TiBr"}],"file_date_updated":"2023-03-30T07:09:35Z","article_processing_charge":"No","abstract":[{"lang":"eng","text":"An improved asymptotic formula is established for the number of rational points of bounded height on the split smooth del Pezzo surface of degree 5. The proof uses the five conic bundle structures on the surface."}],"has_accepted_license":"1","publisher":"State University of New York","intvolume":"        28","author":[{"full_name":"Browning, Timothy D","first_name":"Timothy D","last_name":"Browning","id":"35827D50-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8314-0177"}],"ddc":["510"],"citation":{"mla":"Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del Pezzo Surface of Degree 5.” <i>New York Journal of Mathematics</i>, vol. 28, State University of New York, 2022, pp. 1193–229.","apa":"Browning, T. D. (2022). Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. <i>New York Journal of Mathematics</i>. State University of New York.","ama":"Browning TD. Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. <i>New York Journal of Mathematics</i>. 2022;28:1193-1229.","ieee":"T. D. Browning, “Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5,” <i>New York Journal of Mathematics</i>, vol. 28. State University of New York, pp. 1193–1229, 2022.","chicago":"Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del Pezzo Surface of Degree 5.” <i>New York Journal of Mathematics</i>. State University of New York, 2022.","ista":"Browning TD. 2022. Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. New York Journal of Mathematics. 28, 1193–1229.","short":"T.D. Browning, New York Journal of Mathematics 28 (2022) 1193–1229."},"title":"Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5","day":"24","publication":"New York Journal of Mathematics","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_identifier":{"issn":["1076-9803"]},"page":"1193 - 1229","corr_author":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"date_published":"2022-08-24T00:00:00Z","oa":1,"year":"2022","_id":"12776","oa_version":"Published Version","article_type":"original","volume":28,"type":"journal_article","file":[{"success":1,"date_created":"2023-03-30T07:09:35Z","relation":"main_file","file_name":"2022_NYJM_Browning.pdf","date_updated":"2023-03-30T07:09:35Z","file_id":"12778","checksum":"c01e8291794a1bdb7416aa103cb68ef8","access_level":"open_access","content_type":"application/pdf","file_size":897267,"creator":"dernst"}],"language":[{"iso":"eng"}],"acknowledgement":"This work was begun while the author was participating in the programme on \"Diophantine equations\" at the Hausdorff Research Institute for Mathematics in Bonn in 2009. The hospitality and financial support of the institute is gratefully acknowledged. The idea of using conic bundles to study the split del Pezzo surface of degree 5 was explained to the author by Professor Salberger. The author is very grateful to him for his input into this project and also to Shuntaro Yamagishi for many useful comments on an earlier version of this manuscript. While working on this paper the author was supported by FWF grant P32428-N35."},{"date_updated":"2023-04-25T07:34:49Z","date_published":"2022-03-30T00:00:00Z","fulldoi":"https://doi.org/10.48550/arXiv.2203.16701","publication_status":"submitted","oa":1,"month":"03","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["2203.16701"]},"publication":"arXiv","title":"Towards differential relational privacy and its use in question answering","article_number":"2203.16701","status":"public","day":"30","date_created":"2023-04-23T16:11:48Z","citation":{"ieee":"S. Bombari <i>et al.</i>, “Towards differential relational privacy and its use in question answering,” <i>arXiv</i>. .","apa":"Bombari, S., Achille, A., Wang, Z., Wang, Y.-X., Xie, Y., Singh, K. Y., … Soatto, S. (n.d.). Towards differential relational privacy and its use in question answering. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2203.16701\">https://doi.org/10.48550/arXiv.2203.16701</a>","ama":"Bombari S, Achille A, Wang Z, et al. Towards differential relational privacy and its use in question answering. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2203.16701\">10.48550/arXiv.2203.16701</a>","chicago":"Bombari, Simone, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, and Stefano Soatto. “Towards Differential Relational Privacy and Its Use in Question Answering.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2203.16701\">https://doi.org/10.48550/arXiv.2203.16701</a>.","mla":"Bombari, Simone, et al. “Towards Differential Relational Privacy and Its Use in Question Answering.” <i>ArXiv</i>, 2203.16701, doi:<a href=\"https://doi.org/10.48550/arXiv.2203.16701\">10.48550/arXiv.2203.16701</a>.","ista":"Bombari S, Achille A, Wang Z, Wang Y-X, Xie Y, Singh KY, Appalaraju S, Mahadevan V, Soatto S. Towards differential relational privacy and its use in question answering. arXiv, 2203.16701.","short":"S. Bombari, A. Achille, Z. Wang, Y.-X. Wang, Y. Xie, K.Y. Singh, S. Appalaraju, V. Mahadevan, S. Soatto, ArXiv (n.d.)."},"language":[{"iso":"eng"}],"doi":"10.48550/arXiv.2203.16701","type":"preprint","author":[{"first_name":"Simone","full_name":"Bombari, Simone","last_name":"Bombari","id":"ca726dda-de17-11ea-bc14-f9da834f63aa"},{"last_name":"Achille","full_name":"Achille, Alessandro","first_name":"Alessandro"},{"first_name":"Zijian","full_name":"Wang, Zijian","last_name":"Wang"},{"last_name":"Wang","first_name":"Yu-Xiang","full_name":"Wang, Yu-Xiang"},{"last_name":"Xie","full_name":"Xie, Yusheng","first_name":"Yusheng"},{"full_name":"Singh, Kunwar Yashraj","first_name":"Kunwar Yashraj","last_name":"Singh"},{"first_name":"Srikar","full_name":"Appalaraju, Srikar","last_name":"Appalaraju"},{"last_name":"Mahadevan","first_name":"Vijay","full_name":"Mahadevan, Vijay"},{"full_name":"Soatto, Stefano","first_name":"Stefano","last_name":"Soatto"}],"department":[{"_id":"GradSch"},{"_id":"MaMo"}],"arxiv":1,"year":"2022","_id":"12860","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2203.16701"}],"oa_version":"Preprint","abstract":[{"lang":"eng","text":"Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) to understand, quantify and control this phenomenon. While bounding general memorization can have detrimental effects on the performance of a trained model, bounding RM does not prevent effective learning. The difference is most pronounced when the data distribution is long-tailed, with many queries having only few training examples: Impeding general memorization prevents effective learning, while impeding only relational memorization still allows learning general properties of the underlying concepts. We formalize the notion of Relational Privacy (RP) and, inspired by Differential Privacy (DP), we provide a possible definition of Differential Relational Privacy (DrP). These notions can be used to describe and compute bounds on the amount of RM in a trained model. We illustrate Relational Privacy concepts in experiments with large-scale models for Question Answering."}]},{"title":"Improving genome-wide association discovery and genomic prediction accuracy in biobank data","status":"public","day":"02","date_created":"2023-05-23T16:28:13Z","date_published":"2022-09-02T00:00:00Z","date_updated":"2025-06-12T06:22:36Z","fulldoi":"https://doi.org/10.5061/DRYAD.GTHT76HMZ","oa":1,"month":"09","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","corr_author":"1","related_material":{"record":[{"status":"public","id":"11733","relation":"used_in_publication"}]},"tmp":{"short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"publisher":"Dryad","department":[{"_id":"MaRo"}],"year":"2022","license":"https://creativecommons.org/publicdomain/zero/1.0/","abstract":[{"text":"Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.","lang":"eng"}],"_id":"13064","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.gtht76hmz"}],"oa_version":"Published Version","article_processing_charge":"No","ddc":["570"],"citation":{"short":"E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022).","ista":"Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","chicago":"Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi, Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>.","apa":"Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., &#38; Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>","ieee":"E. Orliac <i>et al.</i>, “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022.","ama":"Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>","mla":"Orliac, Etienne, et al. <i>Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>."},"doi":"10.5061/DRYAD.GTHT76HMZ","type":"research_data_reference","author":[{"last_name":"Orliac","first_name":"Etienne","full_name":"Orliac, Etienne"},{"last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel","first_name":"Daniel"},{"last_name":"Ojavee","full_name":"Ojavee, Sven","first_name":"Sven"},{"first_name":"Kristi","full_name":"Läll, Kristi","last_name":"Läll"},{"last_name":"Mägi","first_name":"Reedik","full_name":"Mägi, Reedik"},{"full_name":"Visscher, Peter","first_name":"Peter","last_name":"Visscher"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}]},{"doi":"10.5061/DRYAD.M905QFV4B","author":[{"last_name":"Koch","full_name":"Koch, Eva","first_name":"Eva"},{"last_name":"Ravinet","full_name":"Ravinet, Mark","first_name":"Mark"},{"first_name":"Anja M","full_name":"Westram, Anja M","orcid":"0000-0003-1050-4969","id":"3C147470-F248-11E8-B48F-1D18A9856A87","last_name":"Westram"},{"full_name":"Jonannesson, Kerstin","first_name":"Kerstin","last_name":"Jonannesson"},{"last_name":"Butlin","first_name":"Roger","full_name":"Butlin, Roger"}],"type":"research_data_reference","citation":{"ista":"Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. 2022. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>.","short":"E. Koch, M. Ravinet, A.M. Westram, K. Jonannesson, R. Butlin, (2022).","ieee":"E. Koch, M. Ravinet, A. M. Westram, K. Jonannesson, and R. Butlin, “Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution.” Dryad, 2022.","ama":"Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>","apa":"Koch, E., Ravinet, M., Westram, A. M., Jonannesson, K., &#38; Butlin, R. (2022). Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">https://doi.org/10.5061/DRYAD.M905QFV4B</a>","chicago":"Koch, Eva, Mark Ravinet, Anja M Westram, Kerstin Jonannesson, and Roger Butlin. “Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">https://doi.org/10.5061/DRYAD.M905QFV4B</a>.","mla":"Koch, Eva, et al. <i>Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>."},"ddc":["570"],"department":[{"_id":"NiBa"}],"_id":"13066","abstract":[{"lang":"eng","text":"Chromosomal inversions have been shown to play a major role in local adaptation by suppressing recombination between alternative arrangements and maintaining beneficial allele combinations. However, so far, their importance relative to the remaining genome remains largely unknown. Understanding the genetic architecture of adaptation requires better estimates of how loci of different effect sizes contribute to phenotypic variation. Here, we used three Swedish islands where the marine snail Littorina saxatilis has repeatedly evolved into two distinct ecotypes along a habitat transition. We estimated the contribution of inversion polymorphisms to phenotypic divergence while controlling for polygenic effects in the remaining genome using a quantitative genetics framework. We confirmed the importance of inversions but showed that contributions of loci outside inversions are of similar magnitude, with variable proportions dependent on the trait and the population. Some inversions showed consistent effects across all sites, whereas others exhibited site-specific effects, indicating that the genomic basis for replicated phenotypic divergence is only partly shared. The contributions of sexual dimorphism as well as environmental factors to phenotypic variation were significant but minor compared to inversions and polygenic background. Overall, this integrated approach provides insight into the multiple mechanisms contributing to parallel phenotypic divergence."}],"main_file_link":[{"url":"https://doi.org/10.5061/dryad.m905qfv4b","open_access":"1"}],"oa_version":"Published Version","article_processing_charge":"No","year":"2022","publisher":"Dryad","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"07","tmp":{"short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"12247"}]},"fulldoi":"https://doi.org/10.5061/DRYAD.M905QFV4B","date_updated":"2023-08-04T09:42:10Z","date_published":"2022-07-28T00:00:00Z","oa":1,"status":"public","title":"Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution","date_created":"2023-05-23T16:33:12Z","day":"28"},{"month":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","corr_author":"1","related_material":{"record":[{"status":"public","id":"11180","relation":"used_in_publication"}],"link":[{"relation":"software","url":"https://github.com/npostnikova/mq-based-schedulers/tree/v1.1"}]},"date_updated":"2025-04-14T13:51:59Z","date_published":"2022-01-03T00:00:00Z","fulldoi":"https://doi.org/10.5281/ZENODO.5733408","oa":1,"title":"Multi-queues can be state-of-the-art priority schedulers","status":"public","day":"03","date_created":"2023-05-23T17:05:40Z","doi":"10.5281/ZENODO.5733408","type":"research_data_reference","author":[{"first_name":"Anastasiia","full_name":"Postnikova, Anastasiia","last_name":"Postnikova"},{"first_name":"Nikita","full_name":"Koval, Nikita","last_name":"Koval","id":"2F4DB10C-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Nadiradze, Giorgi","first_name":"Giorgi","last_name":"Nadiradze","id":"3279A00C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5634-0731"},{"last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian"}],"ddc":["510"],"citation":{"chicago":"Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. <a href=\"https://doi.org/10.5281/ZENODO.5733408\">https://doi.org/10.5281/ZENODO.5733408</a>.","apa":"Postnikova, A., Koval, N., Nadiradze, G., &#38; Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5733408\">https://doi.org/10.5281/ZENODO.5733408</a>","ama":"Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:<a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>","ieee":"A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.","mla":"Postnikova, Anastasiia, et al. <i>Multi-Queues Can Be State-of-the-Art Priority Schedulers</i>. Zenodo, 2022, doi:<a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>.","ista":"Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>.","short":"A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022)."},"department":[{"_id":"DaAl"}],"year":"2022","_id":"13076","abstract":[{"text":"The source code for replicating experiments presented in the paper.\r\n\r\nThe implementation of the designed priority schedulers can be found in Galois-2.2.1/include/Galois/WorkList/:\r\nStealingMultiQueue.h is the StealingMultiQueue.\r\nMQOptimized/ contains MQ Optimized variants.\r\n\r\nWe provide images that contain all the dependencies and datasets. Images can be pulled from npostnikova/mq-based-schedulers repository, or downloaded from Zenodo. See readme for more detail.","lang":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5813846"}],"article_processing_charge":"No","oa_version":"Published Version","publisher":"Zenodo"},{"language":[{"iso":"eng"}],"acknowledgement":"The authors would like to thank members of the Vogels lab and Manohar lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X and Leverhulme Grant RPG-2018-310.","volume":199,"type":"conference","file":[{"date_updated":"2023-07-18T06:32:38Z","file_id":"13243","relation":"main_file","file_name":"2022_PMLR_vanderPlas.pdf","file_size":585135,"creator":"dernst","access_level":"open_access","checksum":"7530a93ef42e10b4db1e5e4b69796e93","content_type":"application/pdf","success":1,"date_created":"2023-07-18T06:32:38Z"}],"_id":"13239","oa_version":"Published Version","year":"2022","date_published":"2022-12-01T00:00:00Z","oa":1,"scopus_import":"1","publication_identifier":{"eissn":["2640-3498"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"518-531","publication":"Proceedings of Machine Learning Research","title":"Predictive learning enables neural networks to learn complex working memory tasks","day":"01","citation":{"short":"T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 518–531.","ista":"Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables neural networks to learn complex working memory tasks. Proceedings of Machine Learning Research. vol. 199, 518–531.","ama":"Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural networks to learn complex working memory tasks. In: <i>Proceedings of Machine Learning Research</i>. Vol 199. ML Research Press; 2022:518-531.","ieee":"T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning enables neural networks to learn complex working memory tasks,” in <i>Proceedings of Machine Learning Research</i>, 2022, vol. 199, pp. 518–531.","apa":"Van Der Plas, T. L., Vogels, T. P., &#38; Manohar, S. G. (2022). Predictive learning enables neural networks to learn complex working memory tasks. In <i>Proceedings of Machine Learning Research</i> (Vol. 199, pp. 518–531). ML Research Press.","chicago":"Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In <i>Proceedings of Machine Learning Research</i>, 199:518–31. ML Research Press, 2022.","mla":"Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” <i>Proceedings of Machine Learning Research</i>, vol. 199, ML Research Press, 2022, pp. 518–31."},"ddc":["000"],"intvolume":"       199","author":[{"last_name":"Van Der Plas","first_name":"Thijs L.","full_name":"Van Der Plas, Thijs L."},{"first_name":"Tim P","full_name":"Vogels, Tim P","last_name":"Vogels","orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"},{"first_name":"Sanjay G.","full_name":"Manohar, Sanjay G.","last_name":"Manohar"}],"publisher":"ML Research Press","has_accepted_license":"1","department":[{"_id":"TiVo"}],"file_date_updated":"2023-07-18T06:32:38Z","abstract":[{"text":"Brains are thought to engage in predictive learning - learning to predict upcoming stimuli - to construct an internal model of their environment. This is especially notable for spatial navigation, as first described by Tolman’s latent learning tasks. However, predictive learning has also been observed in sensory cortex, in settings unrelated to spatial navigation. Apart from normative frameworks such as active inference or efficient coding, what could be the utility of learning to predict the patterns of occurrence of correlated stimuli? Here we show that prediction, and thereby the construction of an internal model of sequential stimuli, can bootstrap the learning process of a working memory task in a recurrent neural network. We implemented predictive learning alongside working memory match-tasks, and networks emerged to solve the prediction task first by encoding information across time to predict upcoming stimuli, and then eavesdropped on this solution to solve the matching task. Eavesdropping was most beneficial when neural resources were limited. Hence, predictive learning acts as a general neural mechanism to learn to store sensory information that can later be essential for working memory tasks.","lang":"eng"}],"article_processing_charge":"No","date_updated":"2025-04-14T07:54:31Z","publication_status":"published","ec_funded":1,"month":"12","project":[{"call_identifier":"H2020","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"quality_controlled":"1","status":"public","date_created":"2023-07-16T22:01:12Z"}]