[{"_id":"12452","year":"2022","date_created":"2023-01-30T10:47:06Z","article_processing_charge":"No","oa_version":"Published Version","quality_controlled":"1","abstract":[{"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.","lang":"eng"}],"file":[{"file_name":"vorf_main.pdf","file_id":"12453","date_created":"2023-01-30T10:48:18Z","relation":"main_file","date_updated":"2023-01-30T10:48:18Z","checksum":"b60b70bb48700aee709c85a69231821d","content_type":"application/pdf","file_size":5202710,"access_level":"open_access","title":"VoRF: Volumetric Relightable Faces","creator":"bbickel"},{"creator":"bbickel","title":"VoRF: Volumetric Relightable Faces – SUPPLEMENTAL MATERIAL –","access_level":"open_access","file_size":37953188,"content_type":"application/pdf","checksum":"ce5f4ce66eaaa1590ee5df989fca6f61","date_updated":"2023-01-30T10:48:29Z","relation":"supplementary_material","date_created":"2023-01-30T10:48:29Z","file_id":"12454","file_name":"vorf_supp.pdf"},{"creator":"bbickel","access_level":"open_access","checksum":"08aecca434b08fee75ee1efe87943718","content_type":"video/mp4","file_size":57855492,"date_updated":"2023-01-30T10:48:37Z","file_name":"video.mp4","relation":"supplementary_material","date_created":"2023-01-30T10:48:37Z","file_id":"12455"}],"publication_status":"published","oa":1,"publication":"33rd British Machine Vision Conference","day":"01","author":[{"last_name":"Rao","first_name":"Pramod","full_name":"Rao, Pramod"},{"last_name":"B R","first_name":"Mallikarjun","full_name":"B R, Mallikarjun"},{"full_name":"Fox, Gereon","last_name":"Fox","first_name":"Gereon"},{"full_name":"Weyrich, Tim","last_name":"Weyrich","first_name":"Tim"},{"orcid":"0000-0001-6511-9385","id":"49876194-F248-11E8-B48F-1D18A9856A87","full_name":"Bickel, Bernd","first_name":"Bernd","last_name":"Bickel"},{"last_name":"Seidel","first_name":"Hans-Peter","full_name":"Seidel, Hans-Peter"},{"first_name":"Hanspeter","last_name":"Pfister","full_name":"Pfister, Hanspeter"},{"full_name":"Matusik, Wojciech","last_name":"Matusik","first_name":"Wojciech"},{"first_name":"Ayush","last_name":"Tewari","full_name":"Tewari, Ayush"},{"full_name":"Theobalt, Christian","last_name":"Theobalt","first_name":"Christian"},{"last_name":"Elgharib","first_name":"Mohamed","full_name":"Elgharib, Mohamed"}],"citation":{"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.","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.","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.","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.","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."},"date_updated":"2023-10-31T08:40:55Z","article_number":"708","has_accepted_license":"1","title":"VoRF: Volumetric Relightable Faces","scopus_import":"1","acknowledgement":"This work was supported by the ERC Consolidator Grant 4DReply (770784).","conference":{"start_date":"2022-11-21","location":"London, United Kingdom","name":"BMVC: British Machine Vision Conference","end_date":"2022-11-24"},"main_file_link":[{"open_access":"1","url":"https://bmvc2022.mpi-inf.mpg.de/708/"}],"month":"12","publisher":"British Machine Vision Association and Society for Pattern Recognition","department":[{"_id":"BeBi"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-01-30T10:48:37Z","type":"conference","ddc":["000"],"status":"public","language":[{"iso":"eng"}],"date_published":"2022-12-01T00:00:00Z"},{"type":"journal_article","volume":2022,"file_date_updated":"2023-02-02T08:35:52Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","doi":"10.1088/1742-5468/ac9828","date_published":"2022-11-24T00:00:00Z","language":[{"iso":"eng"}],"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["510","530"],"intvolume":"      2022","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.","scopus_import":"1","publisher":"IOP Publishing","department":[{"_id":"MaMo"}],"month":"11","isi":1,"citation":{"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>.","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>","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.","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>","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>.","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)."},"date_updated":"2024-03-07T10:36:52Z","author":[{"id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","first_name":"Marco"},{"first_name":"Ramji","last_name":"Venkataramanan","full_name":"Venkataramanan, Ramji"}],"day":"24","publication_identifier":{"issn":["1742-5468"]},"publication":"Journal of Statistical Mechanics: Theory and Experiment","oa":1,"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"external_id":{"isi":["000889589900001"]},"title":"Approximate message passing with spectral initialization for generalized linear models","has_accepted_license":"1","issue":"11","article_number":"114003","date_created":"2023-02-02T08:31:57Z","year":"2022","_id":"12480","article_type":"original","publication_status":"published","related_material":{"record":[{"id":"10598","status":"public","relation":"earlier_version"}]},"file":[{"date_created":"2023-02-02T08:35:52Z","relation":"main_file","success":1,"file_id":"12481","file_name":"2022_JourStatisticalMechanics_Mondelli.pdf","date_updated":"2023-02-02T08:35:52Z","content_type":"application/pdf","file_size":1729997,"checksum":"01411ffa76d3e380a0446baeb89b1ef7","creator":"dernst","access_level":"open_access"}],"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."}],"quality_controlled":"1","oa_version":"Published Version","keyword":["Statistics","Probability and Uncertainty","Statistics and Probability","Statistical and Nonlinear Physics"],"article_processing_charge":"Yes (via OA deal)"},{"has_accepted_license":"1","external_id":{"arxiv":["2106.11732"]},"title":"FLEA: Provably robust fair multisource learning from unreliable training data","project":[{"name":"Vienna Graduate School on Computational Optimization","_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","grant_number":" W1260-N35"}],"oa":1,"publication":"Transactions on Machine Learning Research","publication_identifier":{"issn":["2835-8856"]},"day":"22","author":[{"last_name":"Iofinova","first_name":"Eugenia B","full_name":"Iofinova, Eugenia B","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","orcid":"0000-0002-7778-3221"},{"last_name":"Konstantinov","first_name":"Nikola H","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","full_name":"Konstantinov, Nikola H"},{"first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"citation":{"ista":"Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research.","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.","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.","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.","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.","short":"E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022)."},"date_updated":"2023-02-23T10:30:54Z","article_processing_charge":"No","oa_version":"Published Version","quality_controlled":"1","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."}],"file":[{"file_name":"2022_TMLR_Iofinova.pdf","file_id":"12673","success":1,"relation":"main_file","date_created":"2023-02-23T10:30:04Z","date_updated":"2023-02-23T10:30:04Z","checksum":"97c8a8470759cab597abb973ca137a3b","file_size":1948063,"content_type":"application/pdf","access_level":"open_access","creator":"dernst"}],"related_material":{"link":[{"description":"source code","url":"https://github.com/ISTAustria-CVML/FLEA","relation":"software"}]},"article_type":"original","publication_status":"published","_id":"12495","year":"2022","date_created":"2023-02-02T20:29:57Z","acknowledged_ssus":[{"_id":"ScienComp"}],"ddc":["000"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","language":[{"iso":"eng"}],"date_published":"2022-12-22T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-02-23T10:30:04Z","type":"journal_article","main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=XsPopigZXV"}],"month":"12","publisher":"ML Research Press","department":[{"_id":"ChLa"}],"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. ","arxiv":1},{"abstract":[{"text":"We explore the notion of history-determinism in the context of timed automata (TA). History-deterministic automata are those in which nondeterminism can be resolved on the fly, based on the run constructed thus far. History-determinism is a robust property that admits different game-based characterisations, and history-deterministic specifications allow for game-based verification without an expensive determinization step.\r\nWe show yet another characterisation of history-determinism in terms of fair simulation, at the general level of labelled transition systems: a system is history-deterministic precisely if and only if it fairly simulates all language smaller systems.\r\nFor timed automata over infinite timed words it is known that universality is undecidable for Büchi TA. We show that for history-deterministic TA with arbitrary parity acceptance, timed universality, inclusion, and synthesis all remain decidable and are ExpTime-complete.\r\nFor the subclass of TA with safety or reachability acceptance, we show that checking whether such an automaton is history-deterministic is decidable (in ExpTime), and history-deterministic TA with safety acceptance are effectively determinizable without introducing new automata states.","lang":"eng"}],"file":[{"date_updated":"2023-02-06T09:21:09Z","file_name":"2022_LIPICs_Henzinger2.pdf","file_id":"12520","success":1,"date_created":"2023-02-06T09:21:09Z","relation":"main_file","access_level":"open_access","creator":"dernst","checksum":"9e97e15628f66b2ad77f535bb0327dee","file_size":717940,"content_type":"application/pdf"}],"publication_status":"published","article_processing_charge":"No","oa_version":"Published Version","quality_controlled":"1","_id":"12508","year":"2022","ec_funded":1,"date_created":"2023-02-05T17:24:23Z","title":"History-deterministic timed automata","project":[{"grant_number":"101020093","call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","name":"Vigilant Algorithmic Monitoring of Software"}],"has_accepted_license":"1","citation":{"mla":"Henzinger, Thomas A., et al. “History-Deterministic Timed Automata.” <i>33rd International Conference on Concurrency Theory</i>, vol. 243, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, p. 14:1-14:21, doi:<a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.14\">10.4230/LIPIcs.CONCUR.2022.14</a>.","ieee":"T. A. Henzinger, K. Lehtinen, and P. Totzke, “History-deterministic timed automata,” in <i>33rd International Conference on Concurrency Theory</i>, Warsaw, Poland, 2022, vol. 243, p. 14:1-14:21.","ama":"Henzinger TA, Lehtinen K, Totzke P. History-deterministic timed automata. In: <i>33rd International Conference on Concurrency Theory</i>. Vol 243. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022:14:1-14:21. doi:<a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.14\">10.4230/LIPIcs.CONCUR.2022.14</a>","apa":"Henzinger, T. A., Lehtinen, K., &#38; Totzke, P. (2022). History-deterministic timed automata. In <i>33rd International Conference on Concurrency Theory</i> (Vol. 243, p. 14:1-14:21). Warsaw, Poland: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.14\">https://doi.org/10.4230/LIPIcs.CONCUR.2022.14</a>","chicago":"Henzinger, Thomas A, Karoliina Lehtinen, and Patrick Totzke. “History-Deterministic Timed Automata.” In <i>33rd International Conference on Concurrency Theory</i>, 243:14:1-14:21. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. <a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.14\">https://doi.org/10.4230/LIPIcs.CONCUR.2022.14</a>.","ista":"Henzinger TA, Lehtinen K, Totzke P. 2022. History-deterministic timed automata. 33rd International Conference on Concurrency Theory. CONCUR: Conference on Concurrency Theory, LIPIcs, vol. 243, 14:1-14:21.","short":"T.A. Henzinger, K. Lehtinen, P. Totzke, in:, 33rd International Conference on Concurrency Theory, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, p. 14:1-14:21."},"author":[{"orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","first_name":"Thomas A"},{"first_name":"Karoliina","last_name":"Lehtinen","full_name":"Lehtinen, Karoliina"},{"full_name":"Totzke, Patrick","first_name":"Patrick","last_name":"Totzke"}],"date_updated":"2023-02-06T09:23:31Z","oa":1,"publication":"33rd International Conference on Concurrency Theory","publication_identifier":{"issn":["1868-8969"],"isbn":["9783959772464"]},"day":"06","month":"09","publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","department":[{"_id":"ToHe"}],"conference":{"location":"Warsaw, Poland","name":"CONCUR: Conference on Concurrency Theory","start_date":"2022-09-13","end_date":"2022-09-16"},"page":"14:1-14:21","scopus_import":"1","acknowledgement":"Thomas A. Henzinger: This work was supported in part by the ERC-2020-AdG 101020093.\r\nPatrick Totzke: acknowledges support from the EPSRC, project no. EP/V025848/1.\r\n","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"language":[{"iso":"eng"}],"date_published":"2022-09-06T00:00:00Z","doi":"10.4230/LIPIcs.CONCUR.2022.14","alternative_title":["LIPIcs"],"intvolume":"       243","ddc":["000"],"volume":243,"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-02-06T09:21:09Z"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-02-06T09:13:04Z","volume":241,"type":"conference","ddc":["000"],"intvolume":"       241","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"language":[{"iso":"eng"}],"date_published":"2022-08-22T00:00:00Z","doi":"10.4230/LIPIcs.MFCS.2022.3","scopus_import":"1","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.","place":"Dagstuhl, Germany","page":"3:1-3:6","conference":{"end_date":"2022-08-26","start_date":"2022-08-22","name":"MFCS: Symposium on Mathematical Foundations of Computer Science","location":"Vienna, Austria"},"month":"08","publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","department":[{"_id":"ToHe"}],"oa":1,"publication_identifier":{"isbn":["9783959772563"],"issn":["1868-8969"]},"publication":"47th International Symposium on Mathematical Foundations of Computer Science","day":"22","date_updated":"2023-02-06T09:16:54Z","citation":{"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>.","ista":"Avni G, Henzinger TA. 2022. An updated survey of bidding games on graphs. 47th International Symposium on Mathematical Foundations of Computer Science. MFCS: Symposium on Mathematical Foundations of Computer ScienceLeibniz International Proceedings in Informatics (LIPIcs) vol. 241, 3:1-3:6.","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>.","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>","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>","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."},"author":[{"id":"463C8BC2-F248-11E8-B48F-1D18A9856A87","full_name":"Avni, Guy","orcid":"0000-0001-5588-8287","first_name":"Guy","last_name":"Avni"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","first_name":"Thomas A","last_name":"Henzinger"}],"has_accepted_license":"1","title":"An updated survey of bidding games on graphs","project":[{"name":"Vigilant Algorithmic Monitoring of Software","call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","grant_number":"101020093"}],"_id":"12509","year":"2022","series_title":"Leibniz International Proceedings in Informatics (LIPIcs)","ec_funded":1,"date_created":"2023-02-05T17:26:01Z","article_processing_charge":"No","oa_version":"Published Version","quality_controlled":"1","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"}],"file":[{"content_type":"application/pdf","file_size":624586,"checksum":"1888ec9421622f9526fbec2de035f132","creator":"dernst","access_level":"open_access","date_created":"2023-02-06T09:13:04Z","relation":"main_file","success":1,"file_id":"12519","file_name":"2022_LIPICs_Avni.pdf","date_updated":"2023-02-06T09:13:04Z"}],"publication_status":"published"},{"main_file_link":[{"url":"https://arxiv.org/abs/2107.08467","open_access":"1"}],"month":"06","department":[{"_id":"ToHe"}],"publisher":"Association for the Advancement of Artificial Intelligence","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).","scopus_import":"1","arxiv":1,"page":"6755-6764","intvolume":"        36","date_published":"2022-06-28T00:00:00Z","doi":"10.1609/aaai.v36i6.20631","status":"public","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","volume":36,"quality_controlled":"1","article_processing_charge":"No","keyword":["General Medicine"],"oa_version":"Preprint","publication_status":"published","article_type":"original","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."}],"date_created":"2023-02-05T17:27:42Z","ec_funded":1,"_id":"12510","year":"2022","issue":"6","title":"GoTube: Scalable statistical verification of continuous-depth models","external_id":{"arxiv":["2107.08467"]},"project":[{"name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"Z211"},{"name":"Vigilant Algorithmic Monitoring of Software","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","grant_number":"101020093","call_identifier":"H2020"}],"day":"28","oa":1,"publication_identifier":{"isbn":["978577358350"],"eissn":["2374-3468"],"issn":["2159-5399"]},"publication":"Proceedings of the AAAI Conference on Artificial Intelligence","date_updated":"2023-09-26T10:46:59Z","author":[{"full_name":"Gruenbacher, Sophie A.","last_name":"Gruenbacher","first_name":"Sophie A."},{"first_name":"Mathias","last_name":"Lechner","full_name":"Lechner, Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Hasani, Ramin","first_name":"Ramin","last_name":"Hasani"},{"full_name":"Rus, Daniela","last_name":"Rus","first_name":"Daniela"},{"last_name":"Henzinger","first_name":"Thomas A","orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","full_name":"Henzinger, Thomas A"},{"first_name":"Scott A.","last_name":"Smolka","full_name":"Smolka, Scott A."},{"last_name":"Grosu","first_name":"Radu","full_name":"Grosu, Radu"}],"citation":{"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>.","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.","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>.","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>","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.","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>","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."}},{"intvolume":"        36","doi":"10.1609/aaai.v36i7.20695","date_published":"2022-06-28T00:00:00Z","language":[{"iso":"eng"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","volume":36,"main_file_link":[{"url":"https://arxiv.org/abs/2112.09495","open_access":"1"}],"department":[{"_id":"ToHe"},{"_id":"KrCh"}],"publisher":"Association for the Advancement of Artificial Intelligence","month":"06","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.","scopus_import":"1","arxiv":1,"page":"7326-7336","issue":"7","project":[{"call_identifier":"H2020","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","name":"Vigilant Algorithmic Monitoring of Software"},{"name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","call_identifier":"H2020"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"665385","name":"International IST Doctoral Program"}],"external_id":{"arxiv":["2112.09495"]},"title":"Stability verification in stochastic control systems via neural network supermartingales","day":"28","publication_identifier":{"eissn":["2374-3468"],"isbn":["9781577358350"],"issn":["2159-5399"]},"publication":"Proceedings of the AAAI Conference on Artificial Intelligence","oa":1,"author":[{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","full_name":"Lechner, Mathias","first_name":"Mathias","last_name":"Lechner"},{"last_name":"Zikelic","first_name":"Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","full_name":"Zikelic, Dorde","orcid":"0000-0002-4681-1699"},{"full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X","first_name":"Krishnendu","last_name":"Chatterjee"},{"first_name":"Thomas A","last_name":"Henzinger","orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}],"date_updated":"2025-07-14T09:09:58Z","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>.","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>","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.","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>","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>."},"quality_controlled":"1","oa_version":"Preprint","keyword":["General Medicine"],"article_processing_charge":"No","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"14539"}]},"article_type":"original","publication_status":"published","abstract":[{"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.","lang":"eng"}],"date_created":"2023-02-05T17:29:50Z","ec_funded":1,"year":"2022","_id":"12511"},{"year":"2022","_id":"12516","date_created":"2023-02-05T23:01:00Z","oa_version":"Preprint","article_processing_charge":"No","quality_controlled":"1","abstract":[{"lang":"eng","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."}],"publication_status":"published","publication":"Theory of Cryptography","publication_identifier":{"eissn":["1611-3349"],"isbn":["9783031223648"],"issn":["0302-9743"]},"oa":1,"day":"21","citation":{"short":"A. Bogdanov, M. Cueto Noval, C. Hoffmann, A. Rosen, in:, Theory of Cryptography, Springer Nature, 2022, pp. 565–592.","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>.","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>.","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>","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>","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."},"date_updated":"2023-08-04T10:39:30Z","author":[{"full_name":"Bogdanov, Andrej","last_name":"Bogdanov","first_name":"Andrej"},{"id":"ffc563a3-f6e0-11ea-865d-e3cce03d17cc","full_name":"Cueto Noval, Miguel","first_name":"Miguel","last_name":"Cueto Noval"},{"id":"0f78d746-dc7d-11ea-9b2f-83f92091afe7","full_name":"Hoffmann, Charlotte","last_name":"Hoffmann","first_name":"Charlotte"},{"first_name":"Alon","last_name":"Rosen","full_name":"Rosen, Alon"}],"title":"Public-Key Encryption from Homogeneous CLWE","external_id":{"isi":["000921318200020"]},"scopus_import":"1","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.","page":"565-592","conference":{"end_date":"2022-11-10","start_date":"2022-11-07","name":"TCC: Theory of Cryptography","location":"Chicago, IL, United States"},"main_file_link":[{"url":"https://eprint.iacr.org/2022/093","open_access":"1"}],"isi":1,"publisher":"Springer Nature","department":[{"_id":"KrPi"}],"month":"12","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","volume":13748,"type":"conference","intvolume":"     13748","alternative_title":["LNCS"],"language":[{"iso":"eng"}],"status":"public","doi":"10.1007/978-3-031-22365-5_20","date_published":"2022-12-21T00:00:00Z"},{"has_accepted_license":"1","ddc":["530"],"date_published":"2022-09-25T00:00:00Z","doi":"10.15479/AT:ISTA:12102","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"title":"Data for \"Majorana-like Coulomb spectroscopy in the absence of zero bias peaks\"","file_date_updated":"2023-02-07T08:18:24Z","day":"25","contributor":[{"id":"C0BB2FAC-D767-11E9-B658-BC13E6697425","contributor_type":"contact_person","last_name":"Valentini","first_name":"Marco"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"research_data","citation":{"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>","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.","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>.","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>.","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)."},"author":[{"first_name":"Marco","last_name":"Valentini","id":"C0BB2FAC-D767-11E9-B658-BC13E6697425","full_name":"Valentini, Marco"},{"full_name":"San-Jose, Pablo","last_name":"San-Jose","first_name":"Pablo"},{"first_name":"Jordi","last_name":"Arbiol","full_name":"Arbiol, Jordi"},{"first_name":"Sara","last_name":"Marti-Sanchez","full_name":"Marti-Sanchez, Sara"},{"full_name":"Botifoll, Marc","last_name":"Botifoll","first_name":"Marc"}],"date_updated":"2024-02-21T12:35:34Z","article_processing_charge":"No","oa_version":"Published Version","month":"09","related_material":{"record":[{"relation":"used_in_publication","id":"12118","status":"public"},{"relation":"used_in_publication","id":"13286","status":"public"}]},"publisher":"Institute of Science and Technology Austria","department":[{"_id":"GeKa"}],"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"}],"file":[{"creator":"dernst","access_level":"open_access","checksum":"0dbd6327bf84c7e81b295c4bc9d12826","content_type":"application/x-zip-compressed","file_size":3609122411,"date_updated":"2023-02-07T08:18:24Z","file_name":"Majorana_like.zip","date_created":"2023-02-07T08:18:24Z","success":1,"relation":"main_file","file_id":"12523"}],"date_created":"2023-02-07T08:13:39Z","_id":"12522","year":"2022"},{"citation":{"short":"J. Barbier, T. Hou, M. Mondelli, M. Saenz, ArXiv (n.d.).","ista":"Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? arXiv, 2205.10009.","chicago":"Barbier, Jean, TianQi Hou, Marco Mondelli, and Manuel Saenz. “The Price of Ignorance: How Much Does It Cost to Forget Noise Structure in Low-Rank Matrix Estimation?” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2205.10009\">https://doi.org/10.48550/arXiv.2205.10009</a>.","apa":"Barbier, J., Hou, T., Mondelli, M., &#38; Saenz, M. (n.d.). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2205.10009\">https://doi.org/10.48550/arXiv.2205.10009</a>","ieee":"J. Barbier, T. Hou, M. Mondelli, and M. Saenz, “The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation?,” <i>arXiv</i>. .","ama":"Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2205.10009\">10.48550/arXiv.2205.10009</a>","mla":"Barbier, Jean, et al. “The Price of Ignorance: How Much Does It Cost to Forget Noise Structure in Low-Rank Matrix Estimation?” <i>ArXiv</i>, 2205.10009, doi:<a href=\"https://doi.org/10.48550/arXiv.2205.10009\">10.48550/arXiv.2205.10009</a>."},"type":"preprint","author":[{"first_name":"Jean","last_name":"Barbier","full_name":"Barbier, Jean"},{"first_name":"TianQi","last_name":"Hou","full_name":"Hou, TianQi"},{"orcid":"0000-0002-3242-7020","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","last_name":"Mondelli","first_name":"Marco"},{"last_name":"Saenz","first_name":"Manuel","full_name":"Saenz, Manuel"}],"date_updated":"2023-02-16T09:41:25Z","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"arXiv","day":"20","status":"public","title":"The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation?","external_id":{"arxiv":["2205.10009"]},"language":[{"iso":"eng"}],"date_published":"2022-05-20T00:00:00Z","doi":"10.48550/arXiv.2205.10009","article_number":"2205.10009","arxiv":1,"_id":"12536","year":"2022","date_created":"2023-02-10T13:45:41Z","abstract":[{"text":"We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms perform when the noise statistics is unknown and hence Gaussian noise is assumed? While the matched Bayes-optimal setting with unstructured noise is well understood, the analysis of this mismatched problem is only at its premises. In this paper, we make a step towards understanding the effect of the strong source of mismatch which is the noise statistics. Our main technical contribution is the rigorous analysis of a Bayes estimator and of an approximate message passing (AMP) algorithm, both of which incorrectly assume a Gaussian setup. The first result exploits the theory of spherical integrals and of low-rank matrix perturbations; the idea behind the second one is to design and analyze an artificial AMP which, by taking advantage of the flexibility in the denoisers, is able to \"correct\" the mismatch. Armed with these sharp asymptotic characterizations, we unveil a rich and often unexpected phenomenology. For example, despite AMP is in principle designed to efficiently compute the Bayes estimator, the former is outperformed by the latter in terms of mean-square error. We show that this performance gap is due to an incorrect estimation of the signal norm. In fact, when the SNR is large enough, the overlaps of the AMP and the Bayes estimator coincide, and they even match those of optimal estimators taking into account the structure of the noise.","lang":"eng"}],"month":"05","publication_status":"accepted","department":[{"_id":"MaMo"}],"article_processing_charge":"No","oa_version":"Preprint","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2205.10009"}]},{"arxiv":1,"page":"7628-7640","acknowledgement":"The authors were partially supported by the 2019 Lopez-Loreta prize, and they would like to thank\r\nQuynh Nguyen, Mahdi Soltanolkotabi and Adel Javanmard for helpful discussions.\r\n","department":[{"_id":"MaMo"}],"publisher":"Curran Associates","month":"07","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2205.10217"}],"type":"conference","volume":35,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-07-24T00:00:00Z","language":[{"iso":"eng"}],"status":"public","intvolume":"        35","date_created":"2023-02-10T13:46:37Z","year":"2022","_id":"12537","publication_status":"published","abstract":[{"lang":"eng","text":"The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks. A line of work has studied the NTK spectrum for two-layer and deep networks with at least a layer with Ω(N) neurons, N being the number of training samples. Furthermore, there is increasing evidence suggesting that deep networks with sub-linear layer widths are powerful memorizers and optimizers, as long as the number of parameters exceeds the number of samples. Thus, a natural open question is whether the NTK is well conditioned in such a challenging sub-linear setup. In this paper, we answer this question in the affirmative. Our key technical contribution is a lower bound on the smallest NTK eigenvalue for deep networks with the minimum possible over-parameterization: the number of parameters is roughly Ω(N) and, hence, the number of neurons is as little as Ω(N−−√). To showcase the applicability of our NTK bounds, we provide two results concerning memorization capacity and optimization guarantees for gradient descent training."}],"quality_controlled":"1","oa_version":"Preprint","article_processing_charge":"No","author":[{"id":"ca726dda-de17-11ea-bc14-f9da834f63aa","full_name":"Bombari, Simone","first_name":"Simone","last_name":"Bombari"},{"first_name":"Mohammad Hossein","last_name":"Amani","full_name":"Amani, Mohammad Hossein"},{"last_name":"Mondelli","first_name":"Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020"}],"citation":{"mla":"Bombari, Simone, et al. “Memorization and Optimization in Deep Neural Networks with Minimum Over-Parameterization.” <i>36th Conference on Neural Information Processing Systems</i>, vol. 35, Curran Associates, 2022, pp. 7628–40.","ieee":"S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in deep neural networks with minimum over-parameterization,” in <i>36th Conference on Neural Information Processing Systems</i>, 2022, vol. 35, pp. 7628–7640.","ama":"Bombari S, Amani MH, Mondelli M. Memorization and optimization in deep neural networks with minimum over-parameterization. In: <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. Curran Associates; 2022:7628-7640.","apa":"Bombari, S., Amani, M. H., &#38; Mondelli, M. (2022). Memorization and optimization in deep neural networks with minimum over-parameterization. In <i>36th Conference on Neural Information Processing Systems</i> (Vol. 35, pp. 7628–7640). Curran Associates.","chicago":"Bombari, Simone, Mohammad Hossein Amani, and Marco Mondelli. “Memorization and Optimization in Deep Neural Networks with Minimum Over-Parameterization.” In <i>36th Conference on Neural Information Processing Systems</i>, 35:7628–40. Curran Associates, 2022.","ista":"Bombari S, Amani MH, Mondelli M. 2022. Memorization and optimization in deep neural networks with minimum over-parameterization. 36th Conference on Neural Information Processing Systems. vol. 35, 7628–7640.","short":"S. Bombari, M.H. Amani, M. Mondelli, in:, 36th Conference on Neural Information Processing Systems, Curran Associates, 2022, pp. 7628–7640."},"date_updated":"2024-09-10T13:03:19Z","day":"24","publication":"36th Conference on Neural Information Processing Systems","publication_identifier":{"isbn":["9781713871088"]},"oa":1,"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"external_id":{"arxiv":["2205.10217"]},"title":"Memorization and optimization in deep neural networks with minimum over-parameterization"},{"year":"2022","_id":"12538","date_created":"2023-02-10T13:47:56Z","oa_version":"Preprint","article_processing_charge":"No","quality_controlled":"1","abstract":[{"lang":"eng","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."}],"article_type":"original","publication_status":"published","publication":"IEEE Information Theory Workshop","publication_identifier":{"isbn":["9781665483414"]},"oa":1,"day":"16","date_updated":"2023-12-18T11:31:47Z","author":[{"first_name":"Mohammad Hossein","last_name":"Amani","full_name":"Amani, Mohammad Hossein"},{"last_name":"Bombari","first_name":"Simone","id":"ca726dda-de17-11ea-bc14-f9da834f63aa","full_name":"Bombari, Simone"},{"first_name":"Marco","last_name":"Mondelli","orcid":"0000-0002-3242-7020","full_name":"Mondelli, Marco","id":"27EB676C-8706-11E9-9510-7717E6697425"},{"first_name":"Rattana","last_name":"Pukdee","full_name":"Pukdee, Rattana"},{"first_name":"Stefano","last_name":"Rini","full_name":"Rini, Stefano"}],"citation":{"short":"M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information Theory Workshop (2022) 588–593.","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.","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>","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>","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>.","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.","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>."},"external_id":{"arxiv":["2205.08199"]},"title":"Sharp asymptotics on the compression of two-layer neural networks","scopus_import":"1","page":"588-593","conference":{"end_date":"2022-11-09","name":"ITW: Information Theory Workshop","start_date":"2022-11-01","location":"Mumbai, India"},"arxiv":1,"main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2205.08199","open_access":"1"}],"department":[{"_id":"MaMo"}],"publisher":"IEEE","month":"11","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","language":[{"iso":"eng"}],"status":"public","doi":"10.1109/ITW54588.2022.9965870","date_published":"2022-11-16T00:00:00Z"},{"publication":"Proceedings of the 39th International Conference on Machine Learning","oa":1,"citation":{"short":"R. Venkataramanan, K. Kögler, M. Mondelli, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022.","ama":"Venkataramanan R, Kögler K, Mondelli M. Estimation in rotationally invariant generalized linear models via approximate message passing. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 162. ML Research Press; 2022.","ieee":"R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally invariant generalized linear models via approximate message passing,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, 2022, vol. 162.","apa":"Venkataramanan, R., Kögler, K., &#38; Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 162). Baltimore, MD, United States: ML Research Press.","mla":"Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 162, 22, ML Research Press, 2022.","ista":"Venkataramanan R, Kögler K, Mondelli M. 2022. Estimation in rotationally invariant generalized linear models via approximate message passing. Proceedings of the 39th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 162, 22.","chicago":"Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” In <i>Proceedings of the 39th International Conference on Machine Learning</i>, Vol. 162. ML Research Press, 2022."},"author":[{"first_name":"Ramji","last_name":"Venkataramanan","full_name":"Venkataramanan, Ramji"},{"first_name":"Kevin","last_name":"Kögler","id":"94ec913c-dc85-11ea-9058-e5051ab2428b","full_name":"Kögler, Kevin"},{"orcid":"0000-0002-3242-7020","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","last_name":"Mondelli","first_name":"Marco"}],"date_updated":"2024-09-10T13:03:17Z","article_number":"22","has_accepted_license":"1","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"title":"Estimation in rotationally invariant generalized linear models via approximate message passing","year":"2022","_id":"12540","date_created":"2023-02-10T13:49:04Z","oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","file":[{"file_size":2341343,"content_type":"application/pdf","checksum":"67436eb0a660789514cdf9db79e84683","creator":"dernst","access_level":"open_access","relation":"main_file","success":1,"date_created":"2023-02-13T10:53:11Z","file_id":"12547","file_name":"2022_PMLR_Venkataramanan.pdf","date_updated":"2023-02-13T10:53:11Z"}],"abstract":[{"text":"We consider the problem of signal estimation in generalized linear models defined via rotationally invariant design matrices. Since these matrices can have an arbitrary spectral distribution, this model is well suited for capturing complex correlation structures which often arise in applications. We propose a novel family of approximate message passing (AMP) algorithms for signal estimation, and rigorously characterize their performance in the high-dimensional limit via a state evolution recursion. Our rotationally invariant AMP has complexity of the same order as the existing AMP derived under the restrictive assumption of a Gaussian design; our algorithm also recovers this existing AMP as a special case. Numerical results showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal in some settings), but obtained with a much lower complexity, as the proposed algorithm does not require a computationally expensive singular value decomposition.","lang":"eng"}],"publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-02-13T10:53:11Z","volume":162,"type":"conference","intvolume":"       162","ddc":["000"],"language":[{"iso":"eng"}],"status":"public","date_published":"2022-01-01T00:00:00Z","acknowledgement":"The authors would like to thank the anonymous reviewers for their helpful comments. KK and MM were partially supported by the 2019 Lopez-Loreta Prize.","conference":{"end_date":"2022-07-23","location":"Baltimore, MD, United States","start_date":"2022-07-17","name":"ICML: International Conference on Machine Learning"},"publisher":"ML Research Press","department":[{"_id":"MaMo"}]},{"volume":36,"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"status":"public","doi":"10.1609/aaai.v36i9.21222","date_published":"2022-06-28T00:00:00Z","intvolume":"        36","conference":{"name":"Conference on Artificial Intelligence","location":"Virtual","start_date":"2022-02-22","end_date":"2022-03-01"},"page":"9858-9867","arxiv":1,"scopus_import":"1","publisher":"Association for the Advancement of Artificial Intelligence","department":[{"_id":"KrCh"}],"month":"06","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2203.01640","open_access":"1"}],"date_updated":"2023-02-20T07:19:12Z","author":[{"id":"b21b0c15-30a2-11eb-80dc-f13ca25802e1","full_name":"Meggendorfer, Tobias","orcid":"0000-0002-1712-2165","first_name":"Tobias","last_name":"Meggendorfer"}],"citation":{"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.","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.","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>.","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>","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>","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.","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":"Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022","publication_identifier":{"isbn":["1577358767"],"eissn":["2374-3468"]},"oa":1,"day":"28","external_id":{"arxiv":["2203.01640"]},"title":"Risk-aware stochastic shortest path","issue":"9","year":"2022","_id":"12568","date_created":"2023-02-19T23:00:56Z","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."}],"publication_status":"published","oa_version":"Preprint","article_processing_charge":"No","quality_controlled":"1"},{"has_accepted_license":"1","ddc":["004"],"article_number":"2210.06434","doi":"10.48550/arXiv.2210.06434","date_published":"2022-10-12T00:00:00Z","language":[{"iso":"eng"}],"title":"Cross-client Label Propagation for transductive federated learning","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"external_id":{"arxiv":["2210.06434"]},"status":"public","file_date_updated":"2023-02-20T08:21:35Z","day":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"arXiv","oa":1,"date_updated":"2023-02-21T08:20:18Z","author":[{"full_name":"Scott, Jonathan A","id":"e499926b-f6e0-11ea-865d-9c63db0031e8","first_name":"Jonathan A","last_name":"Scott"},{"last_name":"Yeo","first_name":"Michelle X","id":"2D82B818-F248-11E8-B48F-1D18A9856A87","full_name":"Yeo, Michelle X"},{"last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"citation":{"mla":"Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive Federated Learning.” <i>ArXiv</i>, 2210.06434, doi:<a href=\"https://doi.org/10.48550/arXiv.2210.06434\">10.48550/arXiv.2210.06434</a>.","ieee":"J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for transductive federated learning,” <i>arXiv</i>. .","apa":"Scott, J. A., Yeo, M. X., &#38; Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2210.06434\">https://doi.org/10.48550/arXiv.2210.06434</a>","ama":"Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2210.06434\">10.48550/arXiv.2210.06434</a>","chicago":"Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client Label Propagation for Transductive Federated Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2210.06434\">https://doi.org/10.48550/arXiv.2210.06434</a>.","ista":"Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. arXiv, 2210.06434.","short":"J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.)."},"type":"preprint","oa_version":"Preprint","article_processing_charge":"No","publication_status":"submitted","department":[{"_id":"ChLa"}],"month":"10","file":[{"access_level":"open_access","creator":"chl","content_type":"application/pdf","file_size":291893,"checksum":"7ab20543fd4393f14fb857ce2e4f03c6","date_updated":"2023-02-20T08:21:35Z","file_id":"12661","success":1,"date_created":"2023-02-20T08:21:35Z","relation":"main_file","file_name":"2210.06434.pdf"}],"abstract":[{"lang":"eng","text":"We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning. XCLP estimates a data graph jointly from the data of multiple clients and computes labels for the unlabeled data by propagating label information across the graph. To avoid clients having to share their data with anyone, XCLP employs two cryptographically secure protocols: secure Hamming distance computation and secure summation. We demonstrate two distinct applications of XCLP within federated learning. In the first, we use it in a one-shot way to predict labels for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled training data in a federated semi-supervised setting. Experiments on both real federated and standard benchmark datasets show that in both applications XCLP achieves higher classification accuracy than alternative approaches."}],"date_created":"2023-02-20T08:21:50Z","year":"2022","_id":"12660","arxiv":1},{"title":"Generalization in Multi-objective machine learning","external_id":{"arxiv":["2208.13499"]},"status":"public","language":[{"iso":"eng"}],"date_published":"2022-08-29T00:00:00Z","doi":"10.48550/arXiv.2208.13499","article_number":"2208.13499","ddc":["004"],"has_accepted_license":"1","type":"preprint","citation":{"short":"P. Súkeník, C. Lampert, ArXiv (n.d.).","mla":"Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” <i>ArXiv</i>, 2208.13499, doi:<a href=\"https://doi.org/10.48550/arXiv.2208.13499\">10.48550/arXiv.2208.13499</a>.","apa":"Súkeník, P., &#38; Lampert, C. (n.d.). Generalization in Multi-objective machine learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2208.13499\">https://doi.org/10.48550/arXiv.2208.13499</a>","ama":"Súkeník P, Lampert C. Generalization in Multi-objective machine learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2208.13499\">10.48550/arXiv.2208.13499</a>","ieee":"P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” <i>arXiv</i>. .","chicago":"Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2208.13499\">https://doi.org/10.48550/arXiv.2208.13499</a>.","ista":"Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv, 2208.13499."},"author":[{"id":"d64d6a8d-eb8e-11eb-b029-96fd216dec3c","full_name":"Súkeník, Peter","first_name":"Peter","last_name":"Súkeník"},{"last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887"}],"date_updated":"2023-02-21T08:24:55Z","oa":1,"publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"29","abstract":[{"lang":"eng","text":"Modern machine learning tasks often require considering not just one but multiple objectives. For example, besides the prediction quality, this could be the efficiency, robustness or fairness of the learned models, or any of their combinations. Multi-objective learning offers a natural framework for handling such problems without having to commit to early trade-offs. Surprisingly, statistical learning theory so far offers almost no insight into the generalization properties of multi-objective learning. In this work, we make first steps to fill this gap: we establish foundational generalization bounds for the multi-objective setting as well as generalization and excess bounds for learning with scalarizations. We also provide the first theoretical analysis of the relation between the Pareto-optimal sets of the true objectives and the Pareto-optimal sets of their empirical approximations from training data. In particular, we show a surprising asymmetry: all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa."}],"month":"08","publication_status":"submitted","department":[{"_id":"ChLa"}],"article_processing_charge":"No","oa_version":"Preprint","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2208.13499","open_access":"1"}],"arxiv":1,"_id":"12662","year":"2022","date_created":"2023-02-20T08:23:06Z"},{"pmid":1,"date_created":"2023-02-23T09:15:57Z","_id":"12670","year":"2022","article_type":"review","publication_status":"published","abstract":[{"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.","lang":"eng"}],"quality_controlled":"1","keyword":["Plant Science","General Biochemistry","Genetics and Molecular Biology","Biochemistry"],"article_processing_charge":"No","oa_version":"Published Version","date_updated":"2023-05-08T10:59:00Z","author":[{"full_name":"He, Shengbo","last_name":"He","first_name":"Shengbo"},{"orcid":"0000-0002-4008-1234","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","full_name":"Feng, Xiaoqi","first_name":"Xiaoqi","last_name":"Feng"}],"citation":{"short":"S. He, X. Feng, Journal of Integrative Plant Biology 64 (2022) 2240–2251.","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.","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>","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>","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>.","ista":"He S, Feng X. 2022. DNA methylation dynamics during germline development. Journal of Integrative Plant Biology. 64(12), 2240–2251.","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>."},"day":"07","oa":1,"publication_identifier":{"issn":["1672-9072"],"eissn":["1744-7909"]},"publication":"Journal of Integrative Plant Biology","external_id":{"pmid":["36478632"]},"title":"DNA methylation dynamics during germline development","issue":"12","page":"2240-2251","scopus_import":"1","month":"12","publisher":"Wiley","department":[{"_id":"XiFe"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1111/jipb.13422"}],"type":"journal_article","volume":64,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-12-07T00:00:00Z","doi":"10.1111/jipb.13422","status":"public","language":[{"iso":"eng"}],"extern":"1","intvolume":"        64"},{"date_published":"2022-11-17T00:00:00Z","doi":"10.1038/s41586-022-05386-6","status":"public","language":[{"iso":"eng"}],"extern":"1","intvolume":"       611","type":"journal_article","volume":611,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"11","publisher":"Springer Nature","department":[{"_id":"XiFe"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41586-022-05386-6"}],"page":"614-622","scopus_import":"1","title":"Histone H2B.8 compacts flowering plant sperm through chromatin phase separation","external_id":{"pmid":["36323776"]},"issue":"7936","author":[{"last_name":"Buttress","first_name":"Toby","full_name":"Buttress, Toby"},{"full_name":"He, Shengbo","last_name":"He","first_name":"Shengbo"},{"last_name":"Wang","first_name":"Liang","full_name":"Wang, Liang"},{"full_name":"Zhou, Shaoli","first_name":"Shaoli","last_name":"Zhou"},{"full_name":"Saalbach, Gerhard","last_name":"Saalbach","first_name":"Gerhard"},{"first_name":"Martin","last_name":"Vickers","full_name":"Vickers, Martin"},{"last_name":"Li","first_name":"Guohong","full_name":"Li, Guohong"},{"full_name":"Li, Pilong","first_name":"Pilong","last_name":"Li"},{"orcid":"0000-0002-4008-1234","full_name":"Feng, Xiaoqi","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","last_name":"Feng","first_name":"Xiaoqi"}],"date_updated":"2023-05-08T10:59:22Z","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.","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>.","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.","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>."},"day":"17","oa":1,"publication":"Nature","publication_identifier":{"eissn":["1476-4687"],"issn":["0028-0836"]},"publication_status":"published","article_type":"original","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."}],"quality_controlled":"1","article_processing_charge":"No","oa_version":"Published Version","date_created":"2023-02-23T09:17:05Z","pmid":1,"_id":"12671","year":"2022"},{"_id":"12677","year":"2022","acknowledgement":"This research was partially supported by the ERC CoG 863818 (ForM-SMArt) grant.","date_created":"2023-02-24T12:21:40Z","ec_funded":1,"arxiv":1,"article_processing_charge":"No","oa_version":"Preprint","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2209.14368","open_access":"1"}],"abstract":[{"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.","lang":"eng"}],"month":"09","department":[{"_id":"GradSch"},{"_id":"KrCh"}],"publication_status":"submitted","oa":1,"publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"28","type":"preprint","date_updated":"2025-07-14T09:09:51Z","citation":{"short":"K. Chatterjee, M. Mohammadi, R.J. Saona Urmeneta, ArXiv (n.d.).","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>.","ista":"Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with near-optimal bounds. arXiv, 2209.14368.","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>.","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>","ieee":"K. Chatterjee, M. Mohammadi, and R. J. Saona Urmeneta, “Repeated prophet inequality with near-optimal bounds,” <i>arXiv</i>. .","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>"},"author":[{"first_name":"Krishnendu","last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X"},{"first_name":"Mona","last_name":"Mohammadi","full_name":"Mohammadi, Mona","id":"4363614d-b686-11ed-a7d5-ac9e4a24bc2e"},{"id":"BD1DF4C4-D767-11E9-B658-BC13E6697425","full_name":"Saona Urmeneta, Raimundo J","orcid":"0000-0001-5103-038X","last_name":"Saona Urmeneta","first_name":"Raimundo J"}],"article_number":"2209.14368","external_id":{"arxiv":["2209.14368"]},"title":"Repeated prophet inequality with near-optimal bounds","status":"public","project":[{"name":"Formal Methods for Stochastic Models: Algorithms and Applications","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","grant_number":"863818","call_identifier":"H2020"}],"language":[{"iso":"eng"}],"date_published":"2022-09-28T00:00:00Z","doi":"10.48550/ARXIV.2209.14368"},{"scopus_import":"1","acknowledgement":"The authors warmly thank Amos Nevo for having presented the authors to each other during\r\na beautiful conference in Goa in February 2016, where the idea of this paper was born. The\r\nfirst author thanks the IHES for two post-doctoral years when most of this paper was discussed,\r\nand the Topology team in Orsay for financial support at the final stage. The first author was\r\nsupported by the EPRSC EP/P026710/1 grant. Finally, we warmly thank the referee for many\r\nvery helpful comments that have improved the readability of this paper.","page":"679-703","arxiv":1,"license":"https://creativecommons.org/licenses/by-nd/4.0/","isi":1,"department":[{"_id":"TiBr"}],"publisher":"Centre Mersenne","month":"01","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","file_date_updated":"2023-02-27T09:10:13Z","volume":34,"type":"journal_article","intvolume":"        34","ddc":["510"],"language":[{"iso":"eng"}],"status":"public","tmp":{"short":"CC BY-ND (4.0)","name":"Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)","image":"/image/cc_by_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nd/4.0/legalcode"},"doi":"10.5802/JTNB.1222","date_published":"2022-01-27T00:00:00Z","year":"2022","_id":"12684","date_created":"2023-02-26T23:01:02Z","oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","file":[{"date_created":"2023-02-27T09:10:13Z","relation":"main_file","success":1,"file_id":"12689","file_name":"2023_JourTheorieNombreBordeaux_Horesh.pdf","date_updated":"2023-02-27T09:10:13Z","content_type":"application/pdf","file_size":870468,"checksum":"08f28fded270251f568f610cf5166d69","creator":"dernst","access_level":"open_access"}],"abstract":[{"lang":"eng","text":"Given a place  ω  of a global function field  K  over a finite field, with associated affine function ring  Rω  and completion  Kω , the aim of this paper is to give an effective joint equidistribution result for renormalized primitive lattice points  (a,b)∈Rω2  in the plane  Kω2 , and for renormalized solutions to the gcd equation  ax+by=1 . The main tools are techniques of Goronik and Nevo for counting lattice points in well-rounded families of subsets. This gives a sharper analog in positive characteristic of a result of Nevo and the first author for the equidistribution of the primitive lattice points in  \\ZZ2 ."}],"publication_status":"published","article_type":"original","publication_identifier":{"eissn":["2118-8572"],"issn":["1246-7405"]},"publication":"Journal de Theorie des Nombres de Bordeaux","oa":1,"day":"27","author":[{"full_name":"Horesh, Tal","id":"C8B7BF48-8D81-11E9-BCA9-F536E6697425","last_name":"Horesh","first_name":"Tal"},{"last_name":"Paulin","first_name":"Frédéric","full_name":"Paulin, Frédéric"}],"citation":{"short":"T. Horesh, F. Paulin, Journal de Theorie Des Nombres de Bordeaux 34 (2022) 679–703.","chicago":"Horesh, Tal, and Frédéric Paulin. “Effective Equidistribution of Lattice Points in Positive Characteristic.” <i>Journal de Theorie Des Nombres de Bordeaux</i>. Centre Mersenne, 2022. <a href=\"https://doi.org/10.5802/JTNB.1222\">https://doi.org/10.5802/JTNB.1222</a>.","ista":"Horesh T, Paulin F. 2022. Effective equidistribution of lattice points in positive characteristic. Journal de Theorie des Nombres de Bordeaux. 34(3), 679–703.","mla":"Horesh, Tal, and Frédéric Paulin. “Effective Equidistribution of Lattice Points in Positive Characteristic.” <i>Journal de Theorie Des Nombres de Bordeaux</i>, vol. 34, no. 3, Centre Mersenne, 2022, pp. 679–703, doi:<a href=\"https://doi.org/10.5802/JTNB.1222\">10.5802/JTNB.1222</a>.","apa":"Horesh, T., &#38; Paulin, F. (2022). Effective equidistribution of lattice points in positive characteristic. <i>Journal de Theorie Des Nombres de Bordeaux</i>. Centre Mersenne. <a href=\"https://doi.org/10.5802/JTNB.1222\">https://doi.org/10.5802/JTNB.1222</a>","ieee":"T. Horesh and F. Paulin, “Effective equidistribution of lattice points in positive characteristic,” <i>Journal de Theorie des Nombres de Bordeaux</i>, vol. 34, no. 3. Centre Mersenne, pp. 679–703, 2022.","ama":"Horesh T, Paulin F. Effective equidistribution of lattice points in positive characteristic. <i>Journal de Theorie des Nombres de Bordeaux</i>. 2022;34(3):679-703. doi:<a href=\"https://doi.org/10.5802/JTNB.1222\">10.5802/JTNB.1222</a>"},"date_updated":"2023-08-04T10:41:40Z","issue":"3","has_accepted_license":"1","title":"Effective equidistribution of lattice points in positive characteristic","external_id":{"arxiv":["2001.01534"],"isi":["000926504300003"]}}]