[{"project":[{"call_identifier":"H2020","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425","name":"ISTplus - Postdoctoral Fellowships"},{"_id":"25C6DC12-B435-11E9-9278-68D0E5697425","name":"Analysis of quantum many-body systems","grant_number":"694227","call_identifier":"H2020"}],"issue":"4","day":"01","page":"653-676","quality_controlled":"1","language":[{"iso":"eng"}],"status":"public","month":"10","publisher":"Mathematical Sciences Publishers","type":"journal_article","date_published":"2021-10-01T00:00:00Z","external_id":{"arxiv":["2005.02098"]},"abstract":[{"text":"We consider the Fröhlich Hamiltonian with large coupling constant α. For initial data of Pekar product form with coherent phonon field and with the electron minimizing the corresponding energy, we provide a norm approximation of the evolution, valid up to times of order α2. The approximation is given in terms of a Pekar product state, evolved through the Landau-Pekar equations, corrected by a Bogoliubov dynamics taking quantum fluctuations into account. This allows us to show that the Landau-Pekar equations approximately describe the evolution of the electron- and one-phonon reduced density matrices under the Fröhlich dynamics up to times of order α2.","lang":"eng"}],"citation":{"ama":"Leopold NK, Mitrouskas DJ, Rademacher SAE, Schlein B, Seiringer R. Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron. <i>Pure and Applied Analysis</i>. 2021;3(4):653-676. doi:<a href=\"https://doi.org/10.2140/paa.2021.3.653\">10.2140/paa.2021.3.653</a>","ista":"Leopold NK, Mitrouskas DJ, Rademacher SAE, Schlein B, Seiringer R. 2021. Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron. Pure and Applied Analysis. 3(4), 653–676.","ieee":"N. K. Leopold, D. J. Mitrouskas, S. A. E. Rademacher, B. Schlein, and R. Seiringer, “Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron,” <i>Pure and Applied Analysis</i>, vol. 3, no. 4. Mathematical Sciences Publishers, pp. 653–676, 2021.","short":"N.K. Leopold, D.J. Mitrouskas, S.A.E. Rademacher, B. Schlein, R. Seiringer, Pure and Applied Analysis 3 (2021) 653–676.","apa":"Leopold, N. K., Mitrouskas, D. J., Rademacher, S. A. E., Schlein, B., &#38; Seiringer, R. (2021). Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron. <i>Pure and Applied Analysis</i>. Mathematical Sciences Publishers. <a href=\"https://doi.org/10.2140/paa.2021.3.653\">https://doi.org/10.2140/paa.2021.3.653</a>","chicago":"Leopold, Nikolai K, David Johannes Mitrouskas, Simone Anna Elvira Rademacher, Benjamin Schlein, and Robert Seiringer. “Landau–Pekar Equations and Quantum Fluctuations for the Dynamics of a Strongly Coupled Polaron.” <i>Pure and Applied Analysis</i>. Mathematical Sciences Publishers, 2021. <a href=\"https://doi.org/10.2140/paa.2021.3.653\">https://doi.org/10.2140/paa.2021.3.653</a>.","mla":"Leopold, Nikolai K., et al. “Landau–Pekar Equations and Quantum Fluctuations for the Dynamics of a Strongly Coupled Polaron.” <i>Pure and Applied Analysis</i>, vol. 3, no. 4, Mathematical Sciences Publishers, 2021, pp. 653–76, doi:<a href=\"https://doi.org/10.2140/paa.2021.3.653\">10.2140/paa.2021.3.653</a>."},"year":"2021","date_created":"2024-01-28T23:01:43Z","_id":"14889","acknowledgement":"Financial support by the European Union’s Horizon 2020 research and innovation programme\r\nunder the Marie Skłodowska-Curie grant agreement No. 754411 (S.R.) and the European\r\nResearch Council under grant agreement No. 694227 (N.L. and R.S.), as well as by the SNSF\r\nEccellenza project PCEFP2 181153 (N.L.), the NCCR SwissMAP (N.L. and B.S.) and by the\r\nDeutsche Forschungsgemeinschaft (DFG) through the Research Training Group 1838: Spectral\r\nTheory and Dynamics of Quantum Systems (D.M.) is gratefully acknowledged. B.S. gratefully\r\nacknowledges financial support from the Swiss National Science Foundation through the Grant\r\n“Dynamical and energetic properties of Bose-Einstein condensates” and from the European\r\nResearch Council through the ERC-AdG CLaQS (grant agreement No 834782). D.M. thanks\r\nMarcel Griesemer for helpful discussions.","publication_identifier":{"issn":["2578-5893"],"eissn":["2578-5885"]},"article_type":"original","scopus_import":"1","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2005.02098","open_access":"1"}],"doi":"10.2140/paa.2021.3.653","oa_version":"Preprint","article_processing_charge":"No","title":"Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron","publication_status":"published","intvolume":"         3","arxiv":1,"date_updated":"2024-02-05T10:02:45Z","ec_funded":1,"publication":"Pure and Applied Analysis","department":[{"_id":"RoSe"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Leopold","first_name":"Nikolai K","full_name":"Leopold, Nikolai K","id":"4BC40BEC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0495-6822"},{"id":"cbddacee-2b11-11eb-a02e-a2e14d04e52d","full_name":"Mitrouskas, David Johannes","first_name":"David Johannes","last_name":"Mitrouskas"},{"last_name":"Rademacher","first_name":"Simone Anna Elvira","full_name":"Rademacher, Simone Anna Elvira","id":"856966FE-A408-11E9-977E-802DE6697425","orcid":"0000-0001-5059-4466"},{"last_name":"Schlein","first_name":"Benjamin","full_name":"Schlein, Benjamin"},{"first_name":"Robert","last_name":"Seiringer","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6781-0521","full_name":"Seiringer, Robert"}],"volume":3,"oa":1},{"language":[{"iso":"eng"}],"status":"public","month":"10","publisher":"Mathematical Sciences Publishers","type":"journal_article","external_id":{"arxiv":["1912.11004"]},"date_published":"2021-10-01T00:00:00Z","project":[{"call_identifier":"H2020","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425","name":"ISTplus - Postdoctoral Fellowships"}],"issue":"4","day":"01","page":"677-726","quality_controlled":"1","intvolume":"         3","arxiv":1,"date_updated":"2024-02-05T09:26:31Z","ec_funded":1,"publication":"Pure and Applied Analysis","department":[{"_id":"RoSe"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Bossmann","first_name":"Lea","full_name":"Bossmann, Lea","orcid":"0000-0002-6854-1343","id":"A2E3BCBE-5FCC-11E9-AA4B-76F3E5697425"},{"first_name":"Sören P","last_name":"Petrat","id":"40AC02DC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-9166-5889","full_name":"Petrat, Sören P"},{"first_name":"Peter","last_name":"Pickl","full_name":"Pickl, Peter"},{"first_name":"Avy","last_name":"Soffer","full_name":"Soffer, Avy"}],"volume":3,"oa":1,"abstract":[{"text":"We consider a system of N interacting bosons in the mean-field scaling regime and construct corrections to the Bogoliubov dynamics that approximate the true N-body dynamics in norm to arbitrary precision. The N-independent corrections are given in terms of the solutions of the Bogoliubov and Hartree equations and satisfy a generalized form of Wick's theorem. We determine the n-point correlation functions of the excitations around the condensate, as well as the reduced densities of the N-body system, to arbitrary accuracy, given only the knowledge of the two-point functions of a quasi-free state and the solution of the Hartree equation. In this way, the complex problem of computing all n-point correlation functions for an interacting N-body system is essentially reduced to the problem of solving the Hartree equation and the PDEs for the Bogoliubov two-point functions.","lang":"eng"}],"citation":{"ista":"Bossmann L, Petrat SP, Pickl P, Soffer A. 2021. Beyond Bogoliubov dynamics. Pure and Applied Analysis. 3(4), 677–726.","ieee":"L. Bossmann, S. P. Petrat, P. Pickl, and A. Soffer, “Beyond Bogoliubov dynamics,” <i>Pure and Applied Analysis</i>, vol. 3, no. 4. Mathematical Sciences Publishers, pp. 677–726, 2021.","ama":"Bossmann L, Petrat SP, Pickl P, Soffer A. Beyond Bogoliubov dynamics. <i>Pure and Applied Analysis</i>. 2021;3(4):677-726. doi:<a href=\"https://doi.org/10.2140/paa.2021.3.677\">10.2140/paa.2021.3.677</a>","chicago":"Bossmann, Lea, Sören P Petrat, Peter Pickl, and Avy Soffer. “Beyond Bogoliubov Dynamics.” <i>Pure and Applied Analysis</i>. Mathematical Sciences Publishers, 2021. <a href=\"https://doi.org/10.2140/paa.2021.3.677\">https://doi.org/10.2140/paa.2021.3.677</a>.","short":"L. Bossmann, S.P. Petrat, P. Pickl, A. Soffer, Pure and Applied Analysis 3 (2021) 677–726.","apa":"Bossmann, L., Petrat, S. P., Pickl, P., &#38; Soffer, A. (2021). Beyond Bogoliubov dynamics. <i>Pure and Applied Analysis</i>. Mathematical Sciences Publishers. <a href=\"https://doi.org/10.2140/paa.2021.3.677\">https://doi.org/10.2140/paa.2021.3.677</a>","mla":"Bossmann, Lea, et al. “Beyond Bogoliubov Dynamics.” <i>Pure and Applied Analysis</i>, vol. 3, no. 4, Mathematical Sciences Publishers, 2021, pp. 677–726, doi:<a href=\"https://doi.org/10.2140/paa.2021.3.677\">10.2140/paa.2021.3.677</a>."},"year":"2021","date_created":"2024-01-28T23:01:43Z","_id":"14890","acknowledgement":"We are grateful for the hospitality of Central China Normal University (CCNU),\r\nwhere parts of this work were done, and thank Phan Th`anh Nam, Simone\r\nRademacher, Robert Seiringer and Stefan Teufel for helpful discussions. L.B. gratefully acknowledges the support by the German Research Foundation (DFG) within the Research\r\nTraining Group 1838 “Spectral Theory and Dynamics of Quantum Systems”, and the funding\r\nfrom the European Union’s Horizon 2020 research and innovation programme under the Marie\r\nSk lodowska-Curie Grant Agreement No. 754411.","publication_identifier":{"issn":["2578-5893"],"eissn":["2578-5885"]},"article_type":"original","scopus_import":"1","doi":"10.2140/paa.2021.3.677","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1912.11004"}],"article_processing_charge":"No","oa_version":"Preprint","publication_status":"published","title":"Beyond Bogoliubov dynamics"},{"abstract":[{"text":"Hybrid zones are narrow geographic regions where different populations, races or interbreeding species meet and mate, producing mixed ‘hybrid’ offspring. They are relatively common and can be found in a diverse range of organisms and environments. The study of hybrid zones has played an important role in our understanding of the origin of species, with hybrid zones having been described as ‘natural laboratories’. This is because they allow us to study,in situ, the conditions and evolutionary forces that enable divergent taxa to remain distinct despite some ongoing gene exchange between them.","lang":"eng"}],"year":"2021","citation":{"mla":"Stankowski, Sean, et al. “Hybrid Zones.” <i>Encyclopedia of Life Sciences</i>, vol. 2, Wiley, 2021, doi:<a href=\"https://doi.org/10.1002/9780470015902.a0029355\">10.1002/9780470015902.a0029355</a>.","chicago":"Stankowski, Sean, Daria Shipilina, and Anja M Westram. “Hybrid Zones.” In <i>Encyclopedia of Life Sciences</i>, Vol. 2. ELS. Wiley, 2021. <a href=\"https://doi.org/10.1002/9780470015902.a0029355\">https://doi.org/10.1002/9780470015902.a0029355</a>.","short":"S. Stankowski, D. Shipilina, A.M. Westram, in:, Encyclopedia of Life Sciences, Wiley, 2021.","apa":"Stankowski, S., Shipilina, D., &#38; Westram, A. M. (2021). Hybrid Zones. In <i>Encyclopedia of Life Sciences</i> (Vol. 2). Wiley. <a href=\"https://doi.org/10.1002/9780470015902.a0029355\">https://doi.org/10.1002/9780470015902.a0029355</a>","ieee":"S. Stankowski, D. Shipilina, and A. M. Westram, “Hybrid Zones,” in <i>Encyclopedia of Life Sciences</i>, vol. 2, Wiley, 2021.","ista":"Stankowski S, Shipilina D, Westram AM. 2021.Hybrid Zones. In: Encyclopedia of Life Sciences. vol. 2.","ama":"Stankowski S, Shipilina D, Westram AM. Hybrid Zones. In: <i>Encyclopedia of Life Sciences</i>. Vol 2. eLS. Wiley; 2021. doi:<a href=\"https://doi.org/10.1002/9780470015902.a0029355\">10.1002/9780470015902.a0029355</a>"},"_id":"14984","date_created":"2024-02-14T12:05:50Z","publication_identifier":{"isbn":["9780470016176"],"eisbn":["9780470015902"]},"day":"28","doi":"10.1002/9780470015902.a0029355","oa_version":"None","article_processing_charge":"No","quality_controlled":"1","publication_status":"published","title":"Hybrid Zones","intvolume":"         2","language":[{"iso":"eng"}],"publication":"Encyclopedia of Life Sciences","status":"public","date_updated":"2024-02-19T09:54:18Z","month":"05","department":[{"_id":"NiBa"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Wiley","author":[{"id":"43161670-5719-11EA-8025-FABC3DDC885E","full_name":"Stankowski, Sean","first_name":"Sean","last_name":"Stankowski"},{"last_name":"Shipilina","first_name":"Daria","full_name":"Shipilina, Daria","id":"428A94B0-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-1145-9226"},{"last_name":"Westram","first_name":"Anja M","full_name":"Westram, Anja M","orcid":"0000-0003-1050-4969","id":"3C147470-F248-11E8-B48F-1D18A9856A87"}],"volume":2,"date_published":"2021-05-28T00:00:00Z","series_title":"eLS","type":"book_chapter"},{"type":"book_chapter","date_published":"2021-10-13T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"publisher":"Springer","date_updated":"2024-02-19T10:59:04Z","publication":"Computer Vision","status":"public","department":[{"_id":"ChLa"}],"month":"10","language":[{"iso":"eng"}],"oa_version":"None","article_processing_charge":"No","publication_status":"published","quality_controlled":"1","title":"Zero-Shot Learning","publication_identifier":{"isbn":["9783030634155"],"eisbn":["9783030634162"]},"day":"13","edition":"2","page":"1395-1397","doi":"10.1007/978-3-030-63416-2_874","date_created":"2024-02-14T14:05:32Z","_id":"14987","editor":[{"full_name":"Ikeuchi, Katsushi","last_name":"Ikeuchi","first_name":"Katsushi"}],"abstract":[{"lang":"eng","text":"The goal of zero-shot learning is to construct a classifier that can identify object classes for which no training examples are available. When training data for some of the object classes is available but not for others, the name generalized zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot is also used to describe other machine learning-based approaches that require no training data from the problem of interest, such as zero-shot action recognition or zero-shot machine translation."}],"citation":{"mla":"Lampert, Christoph. “Zero-Shot Learning.” <i>Computer Vision</i>, edited by Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:<a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">10.1007/978-3-030-63416-2_874</a>.","chicago":"Lampert, Christoph. “Zero-Shot Learning.” In <i>Computer Vision</i>, edited by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">https://doi.org/10.1007/978-3-030-63416-2_874</a>.","short":"C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397.","apa":"Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), <i>Computer Vision</i> (2nd ed., pp. 1395–1397). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">https://doi.org/10.1007/978-3-030-63416-2_874</a>","ama":"Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. <i>Computer Vision</i>. 2nd ed. Cham: Springer; 2021:1395-1397. doi:<a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">10.1007/978-3-030-63416-2_874</a>","ieee":"C. Lampert, “Zero-Shot Learning,” in <i>Computer Vision</i>, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.","ista":"Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397."},"year":"2021","place":"Cham"},{"day":"01","doi":"10.5281/ZENODO.5747100","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5747100"}],"oa_version":"Published Version","article_processing_charge":"No","title":"Raw data from Johnson et al, PNAS, 2021","abstract":[{"lang":"eng","text":"Raw data generated from the publication - The TPLATE complex mediates membrane bending during plant clathrin-mediated endocytosis by Johnson et al., 2021 In PNAS"}],"year":"2021","citation":{"mla":"Johnson, Alexander J. <i>Raw Data from Johnson et Al, PNAS, 2021</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5747100\">10.5281/ZENODO.5747100</a>.","short":"A.J. Johnson, (2021).","apa":"Johnson, A. J. (2021). Raw data from Johnson et al, PNAS, 2021. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5747100\">https://doi.org/10.5281/ZENODO.5747100</a>","chicago":"Johnson, Alexander J. “Raw Data from Johnson et Al, PNAS, 2021.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5747100\">https://doi.org/10.5281/ZENODO.5747100</a>.","ama":"Johnson AJ. Raw data from Johnson et al, PNAS, 2021. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5747100\">10.5281/ZENODO.5747100</a>","ista":"Johnson AJ. 2021. Raw data from Johnson et al, PNAS, 2021, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5747100\">10.5281/ZENODO.5747100</a>.","ieee":"A. J. Johnson, “Raw data from Johnson et al, PNAS, 2021.” Zenodo, 2021."},"_id":"14988","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"has_accepted_license":"1","date_created":"2024-02-14T14:13:48Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Zenodo","author":[{"orcid":"0000-0002-2739-8843","id":"46A62C3A-F248-11E8-B48F-1D18A9856A87","full_name":"Johnson, Alexander J","first_name":"Alexander J","last_name":"Johnson"}],"oa":1,"related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"9887"}]},"date_published":"2021-12-01T00:00:00Z","type":"research_data_reference","ddc":["580"],"status":"public","date_updated":"2024-02-19T11:06:09Z","month":"12","department":[{"_id":"JiFr"}]},{"arxiv":1,"intvolume":"         2","department":[{"_id":"LaEr"}],"publication":"Probability and Mathematical Physics","ec_funded":1,"date_updated":"2024-02-19T08:30:00Z","author":[{"id":"36D3D8B6-F248-11E8-B48F-1D18A9856A87","full_name":"Alt, Johannes","first_name":"Johannes","last_name":"Alt"},{"orcid":"0000-0001-5366-9603","id":"4DBD5372-F248-11E8-B48F-1D18A9856A87","full_name":"Erdös, László","first_name":"László","last_name":"Erdös"},{"id":"3020C786-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4821-3297","full_name":"Krüger, Torben H","first_name":"Torben H","last_name":"Krüger"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"volume":2,"year":"2021","citation":{"mla":"Alt, Johannes, et al. “Spectral Radius of Random Matrices with Independent Entries.” <i>Probability and Mathematical Physics</i>, vol. 2, no. 2, Mathematical Sciences Publishers, 2021, pp. 221–80, doi:<a href=\"https://doi.org/10.2140/pmp.2021.2.221\">10.2140/pmp.2021.2.221</a>.","apa":"Alt, J., Erdös, L., &#38; Krüger, T. H. (2021). Spectral radius of random matrices with independent entries. <i>Probability and Mathematical Physics</i>. Mathematical Sciences Publishers. <a href=\"https://doi.org/10.2140/pmp.2021.2.221\">https://doi.org/10.2140/pmp.2021.2.221</a>","chicago":"Alt, Johannes, László Erdös, and Torben H Krüger. “Spectral Radius of Random Matrices with Independent Entries.” <i>Probability and Mathematical Physics</i>. Mathematical Sciences Publishers, 2021. <a href=\"https://doi.org/10.2140/pmp.2021.2.221\">https://doi.org/10.2140/pmp.2021.2.221</a>.","short":"J. Alt, L. Erdös, T.H. Krüger, Probability and Mathematical Physics 2 (2021) 221–280.","ieee":"J. Alt, L. Erdös, and T. H. Krüger, “Spectral radius of random matrices with independent entries,” <i>Probability and Mathematical Physics</i>, vol. 2, no. 2. Mathematical Sciences Publishers, pp. 221–280, 2021.","ista":"Alt J, Erdös L, Krüger TH. 2021. Spectral radius of random matrices with independent entries. Probability and Mathematical Physics. 2(2), 221–280.","ama":"Alt J, Erdös L, Krüger TH. Spectral radius of random matrices with independent entries. <i>Probability and Mathematical Physics</i>. 2021;2(2):221-280. doi:<a href=\"https://doi.org/10.2140/pmp.2021.2.221\">10.2140/pmp.2021.2.221</a>"},"abstract":[{"lang":"eng","text":"We consider random n×n matrices X with independent and centered entries and a general variance profile. We show that the spectral radius of X converges with very high probability to the square root of the spectral radius of the variance matrix of X when n tends to infinity. We also establish the optimal rate of convergence, that is a new result even for general i.i.d. matrices beyond the explicitly solvable Gaussian cases. The main ingredient is the proof of the local inhomogeneous circular law [arXiv:1612.07776] at the spectral edge."}],"_id":"15013","date_created":"2024-02-18T23:01:03Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1907.13631"}],"doi":"10.2140/pmp.2021.2.221","scopus_import":"1","article_type":"original","publication_identifier":{"eissn":["2690-1005"],"issn":["2690-0998"]},"acknowledgement":"Partially supported by ERC Starting Grant RandMat No. 715539 and the SwissMap grant of Swiss National Science Foundation. Partially supported by ERC Advanced Grant RanMat No. 338804. Partially supported by the Hausdorff Center for Mathematics in Bonn.","publication_status":"published","title":"Spectral radius of random matrices with independent entries","article_processing_charge":"No","oa_version":"Preprint","language":[{"iso":"eng"}],"month":"05","status":"public","publisher":"Mathematical Sciences Publishers","date_published":"2021-05-21T00:00:00Z","external_id":{"arxiv":["1907.13631"]},"type":"journal_article","issue":"2","project":[{"name":"Random matrices, universality and disordered quantum systems","_id":"258DCDE6-B435-11E9-9278-68D0E5697425","grant_number":"338804","call_identifier":"FP7"}],"page":"221-280","day":"21","quality_controlled":"1"},{"publisher":"Institute of Electrical and Electronics Engineers","type":"journal_article","date_published":"2021-05-01T00:00:00Z","external_id":{"arxiv":["2102.11107"]},"extern":"1","keyword":["Electrical and Electronic Engineering"],"language":[{"iso":"eng"}],"status":"public","month":"05","day":"01","page":"612-634","quality_controlled":"1","issue":"5","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Scholkopf, Bernhard","last_name":"Scholkopf","first_name":"Bernhard"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Bauer, Stefan","last_name":"Bauer","first_name":"Stefan"},{"first_name":"Nan Rosemary","last_name":"Ke","full_name":"Ke, Nan Rosemary"},{"full_name":"Kalchbrenner, Nal","last_name":"Kalchbrenner","first_name":"Nal"},{"first_name":"Anirudh","last_name":"Goyal","full_name":"Goyal, Anirudh"},{"first_name":"Yoshua","last_name":"Bengio","full_name":"Bengio, Yoshua"}],"volume":109,"oa":1,"intvolume":"       109","arxiv":1,"date_updated":"2023-09-11T11:43:35Z","publication":"Proceedings of the IEEE","department":[{"_id":"FrLo"}],"publication_identifier":{"issn":["0018-9219"],"eissn":["1558-2256"]},"article_type":"original","scopus_import":"1","main_file_link":[{"url":"https://doi.org/10.1109/JPROC.2021.3058954","open_access":"1"}],"doi":"10.1109/jproc.2021.3058954","article_processing_charge":"No","oa_version":"Published Version","publication_status":"published","title":"Toward causal representation learning","abstract":[{"lang":"eng","text":"The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities."}],"citation":{"chicago":"Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>.","short":"B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.","apa":"Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., &#38; Bengio, Y. (2021). Toward causal representation learning. <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>","mla":"Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>.","ista":"Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634.","ieee":"B. Scholkopf <i>et al.</i>, “Toward causal representation learning,” <i>Proceedings of the IEEE</i>, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021.","ama":"Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. <i>Proceedings of the IEEE</i>. 2021;109(5):612-634. doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>"},"year":"2021","date_created":"2023-08-21T12:19:30Z","_id":"14117"},{"day":"01","page":"11964-11974","quality_controlled":"1","alternative_title":["PMLR"],"extern":"1","language":[{"iso":"eng"}],"status":"public","month":"08","publisher":"ML Research Press","type":"conference","date_published":"2021-08-01T00:00:00Z","external_id":{"arxiv":["2106.05142"]},"abstract":[{"text":"Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series data augmentation techniques with a novel contrastive\r\nlearning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series.","lang":"eng"}],"citation":{"ista":"Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive learning applied to online patient monitoring. Proceedings of 38th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 139, 11964–11974.","ieee":"H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in <i>Proceedings of 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 11964–11974.","ama":"Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive learning applied to online patient monitoring. In: <i>Proceedings of 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:11964-11974.","mla":"Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” <i>Proceedings of 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 11964–74.","chicago":"Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” In <i>Proceedings of 38th International Conference on Machine Learning</i>, 139:11964–74. ML Research Press, 2021.","short":"H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.","apa":"Yèche, H., Dresdner, G., Locatello, F., Hüser, M., &#38; Rätsch, G. (2021). Neighborhood contrastive learning applied to online patient monitoring. In <i>Proceedings of 38th International Conference on Machine Learning</i> (Vol. 139, pp. 11964–11974). Virtual: ML Research Press."},"year":"2021","date_created":"2023-08-22T14:03:04Z","_id":"14176","conference":{"location":"Virtual","name":"International Conference on Machine Learning","end_date":"2021-07-24","start_date":"2021-07-18"},"scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2106.05142"}],"oa_version":"Preprint","article_processing_charge":"No","publication_status":"published","title":"Neighborhood contrastive learning applied to online patient monitoring","intvolume":"       139","arxiv":1,"date_updated":"2023-09-11T10:16:55Z","publication":"Proceedings of 38th International Conference on Machine Learning","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Yèche, Hugo","last_name":"Yèche","first_name":"Hugo"},{"full_name":"Dresdner, Gideon","last_name":"Dresdner","first_name":"Gideon"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"},{"last_name":"Hüser","first_name":"Matthias","full_name":"Hüser, Matthias"},{"last_name":"Rätsch","first_name":"Gunnar","full_name":"Rätsch, Gunnar"}],"volume":139,"oa":1},{"quality_controlled":"1","day":"01","page":"10401-10412","status":"public","month":"08","extern":"1","alternative_title":["PMLR"],"language":[{"iso":"eng"}],"type":"conference","external_id":{"arxiv":["2006.07886"]},"date_published":"2021-08-01T00:00:00Z","publisher":"ML Research Press","date_created":"2023-08-22T14:03:47Z","_id":"14177","conference":{"start_date":"2021-07-18","location":"Virtual","name":"ICML: International Conference on Machine Learning","end_date":"2021-07-24"},"abstract":[{"lang":"eng","text":"The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained model with a small number of labels."}],"citation":{"mla":"Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated Data.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 10401–12.","apa":"Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., … Bauer, S. (2021). On disentangled representations learned from correlated data. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 10401–10412). Virtual: ML Research Press.","short":"F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.","chicago":"Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled Representations Learned from Correlated Data.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:10401–12. ML Research Press, 2021.","ama":"Träuble F, Creager E, Kilbertus N, et al. On disentangled representations learned from correlated data. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:10401-10412.","ista":"Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf B, Bauer S. 2021. On disentangled representations learned from correlated data. Proceedings of the 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 139, 10401–10412.","ieee":"F. Träuble <i>et al.</i>, “On disentangled representations learned from correlated data,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 10401–10412."},"year":"2021","oa_version":"Published Version","article_processing_charge":"No","publication_status":"published","title":"On disentangled representations learned from correlated data","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2006.07886"}],"date_updated":"2023-09-11T10:18:48Z","publication":"Proceedings of the 38th International Conference on Machine Learning","department":[{"_id":"FrLo"}],"intvolume":"       139","arxiv":1,"volume":139,"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Träuble","first_name":"Frederik","full_name":"Träuble, Frederik"},{"full_name":"Creager, Elliot","last_name":"Creager","first_name":"Elliot"},{"full_name":"Kilbertus, Niki","last_name":"Kilbertus","first_name":"Niki"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Dittadi, Andrea","last_name":"Dittadi","first_name":"Andrea"},{"full_name":"Goyal, Anirudh","first_name":"Anirudh","last_name":"Goyal"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"full_name":"Bauer, Stefan","first_name":"Stefan","last_name":"Bauer"}]},{"main_file_link":[{"url":"https://arxiv.org/abs/2010.14407","open_access":"1"}],"day":"04","publication_status":"published","quality_controlled":"1","title":"On the transfer of disentangled representations in realistic settings","article_processing_charge":"No","oa_version":"Preprint","year":"2021","citation":{"mla":"Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in Realistic Settings.” <i>The Ninth International Conference on Learning Representations</i>, 2021.","chicago":"Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer of Disentangled Representations in Realistic Settings.” In <i>The Ninth International Conference on Learning Representations</i>, 2021.","short":"A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.","apa":"Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., … Schölkopf, B. (2021). On the transfer of disentangled representations in realistic settings. In <i>The Ninth International Conference on Learning Representations</i>. Virtual.","ieee":"A. Dittadi <i>et al.</i>, “On the transfer of disentangled representations in realistic settings,” in <i>The Ninth International Conference on Learning Representations</i>, Virtual, 2021.","ista":"Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic settings. The Ninth International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","ama":"Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled representations in realistic settings. In: <i>The Ninth International Conference on Learning Representations</i>. ; 2021."},"abstract":[{"text":"Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance.","lang":"eng"}],"conference":{"start_date":"2021-05-03","end_date":"2021-05-07","location":"Virtual","name":"ICLR: International Conference on Learning Representations"},"_id":"14178","date_created":"2023-08-22T14:04:16Z","author":[{"full_name":"Dittadi, Andrea","last_name":"Dittadi","first_name":"Andrea"},{"first_name":"Frederik","last_name":"Träuble","full_name":"Träuble, Frederik"},{"first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Wüthrich, Manuel","last_name":"Wüthrich","first_name":"Manuel"},{"full_name":"Agrawal, Vaibhav","last_name":"Agrawal","first_name":"Vaibhav"},{"last_name":"Winther","first_name":"Ole","full_name":"Winther, Ole"},{"full_name":"Bauer, Stefan","first_name":"Stefan","last_name":"Bauer"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2021-05-04T00:00:00Z","external_id":{"arxiv":["2010.14407"]},"type":"conference","oa":1,"language":[{"iso":"eng"}],"arxiv":1,"extern":"1","month":"05","department":[{"_id":"FrLo"}],"publication":"The Ninth International Conference on Learning Representations","status":"public","date_updated":"2023-09-11T10:55:30Z"},{"day":"08","page":"16451-16467","quality_controlled":"1","extern":"1","language":[{"iso":"eng"}],"status":"public","month":"06","date_published":"2021-06-08T00:00:00Z","external_id":{"arxiv":["2106.04619"]},"type":"conference","abstract":[{"lang":"eng","text":"Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice."}],"year":"2021","citation":{"ama":"Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with data augmentations provably isolates content from style. In: <i>Advances in Neural Information Processing Systems</i>. Vol 34. ; 2021:16451-16467.","ieee":"J. von Kügelgen <i>et al.</i>, “Self-supervised learning with data augmentations provably isolates content from style,” in <i>Advances in Neural Information Processing Systems</i>, Virtual, 2021, vol. 34, pp. 16451–16467.","ista":"Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M, Locatello F. 2021. Self-supervised learning with data augmentations provably isolates content from style. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 16451–16467.","short":"J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.","apa":"Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., &#38; Locatello, F. (2021). Self-supervised learning with data augmentations provably isolates content from style. In <i>Advances in Neural Information Processing Systems</i> (Vol. 34, pp. 16451–16467). Virtual.","chicago":"Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” In <i>Advances in Neural Information Processing Systems</i>, 34:16451–67, 2021.","mla":"Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” <i>Advances in Neural Information Processing Systems</i>, vol. 34, 2021, pp. 16451–67."},"_id":"14179","date_created":"2023-08-22T14:04:36Z","conference":{"end_date":"2021-12-10","location":"Virtual","name":"NeurIPS: Neural Information Processing Systems","start_date":"2021-12-07"},"publication_identifier":{"isbn":["9781713845393"]},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2106.04619"}],"article_processing_charge":"No","oa_version":"Preprint","publication_status":"published","title":"Self-supervised learning with data augmentations provably isolates content from style","intvolume":"        34","arxiv":1,"publication":"Advances in Neural Information Processing Systems","date_updated":"2023-09-11T10:33:19Z","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Kügelgen, Julius von","first_name":"Julius von","last_name":"Kügelgen"},{"full_name":"Sharma, Yash","last_name":"Sharma","first_name":"Yash"},{"last_name":"Gresele","first_name":"Luigi","full_name":"Gresele, Luigi"},{"first_name":"Wieland","last_name":"Brendel","full_name":"Brendel, Wieland"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"full_name":"Besserve, Michel","first_name":"Michel","last_name":"Besserve"},{"last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"}],"oa":1,"volume":34},{"author":[{"first_name":"Nasim","last_name":"Rahaman","full_name":"Rahaman, Nasim"},{"full_name":"Gondal, Muhammad Waleed","last_name":"Gondal","first_name":"Muhammad Waleed"},{"last_name":"Joshi","first_name":"Shruti","full_name":"Joshi, Shruti"},{"last_name":"Gehler","first_name":"Peter","full_name":"Gehler, Peter"},{"first_name":"Yoshua","last_name":"Bengio","full_name":"Bengio, Yoshua"},{"first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"volume":34,"arxiv":1,"intvolume":"        34","department":[{"_id":"FrLo"}],"publication":"Advances in Neural Information Processing Systems","date_updated":"2023-09-11T11:33:46Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2110.06399","open_access":"1"}],"publication_identifier":{"isbn":["9781713845393"]},"publication_status":"published","title":"Dynamic inference with neural interpreters","article_processing_charge":"No","oa_version":"Preprint","year":"2021","citation":{"mla":"Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” <i>Advances in Neural Information Processing Systems</i>, vol. 34, 2021, pp. 10985–98.","short":"N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.","apa":"Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F., &#38; Schölkopf, B. (2021). Dynamic inference with neural interpreters. In <i>Advances in Neural Information Processing Systems</i> (Vol. 34, pp. 10985–10998). Virtual.","chicago":"Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural Interpreters.” In <i>Advances in Neural Information Processing Systems</i>, 34:10985–98, 2021.","ama":"Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters. In: <i>Advances in Neural Information Processing Systems</i>. Vol 34. ; 2021:10985-10998.","ista":"Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.","ieee":"N. Rahaman <i>et al.</i>, “Dynamic inference with neural interpreters,” in <i>Advances in Neural Information Processing Systems</i>, Virtual, 2021, vol. 34, pp. 10985–10998."},"abstract":[{"text":"Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \\emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization. ","lang":"eng"}],"conference":{"start_date":"2021-12-07","location":"Virtual","name":"NeurIPS: Neural Information Processing Systems","end_date":"2021-12-10"},"_id":"14180","date_created":"2023-08-22T14:04:55Z","external_id":{"arxiv":["2110.06399"]},"date_published":"2021-10-12T00:00:00Z","type":"conference","language":[{"iso":"eng"}],"extern":"1","month":"10","status":"public","page":"10985-10998","day":"12","quality_controlled":"1"},{"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2105.09240","open_access":"1"}],"doi":"10.24963/ijcai.2021/322","publication_identifier":{"eisbn":["9780999241196"]},"title":"Boosting variational inference with locally adaptive step-sizes","publication_status":"published","article_processing_charge":"No","oa_version":"Published Version","year":"2021","citation":{"mla":"Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–43, doi:<a href=\"https://doi.org/10.24963/ijcai.2021/322\">10.24963/ijcai.2021/322</a>.","chicago":"Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” In <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>, 2337–43. International Joint Conferences on Artificial Intelligence, 2021. <a href=\"https://doi.org/10.24963/ijcai.2021/322\">https://doi.org/10.24963/ijcai.2021/322</a>.","short":"G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.","apa":"Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., &#38; Rätsch, G. (2021). Boosting variational inference with locally adaptive step-sizes. In <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i> (pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial Intelligence. <a href=\"https://doi.org/10.24963/ijcai.2021/322\">https://doi.org/10.24963/ijcai.2021/322</a>","ista":"Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conference on Artificial Intelligence, 2337–2343.","ieee":"G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>, Montreal, Canada, 2021, pp. 2337–2343.","ama":"Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational inference with locally adaptive step-sizes. In: <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>. International Joint Conferences on Artificial Intelligence; 2021:2337-2343. doi:<a href=\"https://doi.org/10.24963/ijcai.2021/322\">10.24963/ijcai.2021/322</a>"},"abstract":[{"text":"Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. The main obstacle to widespread adoption of Boosting Variational Inference is the amount of resources necessary to improve over a strong Variational Inference baseline. In our work, we trace this limitation back to the global curvature of the KL-divergence. We characterize how the global curvature impacts time and memory consumption, address the problem with the notion of local curvature, and provide a novel approximate backtracking algorithm for estimating local curvature. We give new theoretical convergence rates for our algorithms and provide experimental validation on synthetic and real-world datasets.","lang":"eng"}],"conference":{"start_date":"2021-08-19","name":"IJCAI: International Joint Conference on Artificial Intelligence","location":"Montreal, Canada","end_date":"2021-08-27"},"_id":"14181","date_created":"2023-08-22T14:05:14Z","author":[{"full_name":"Dresdner, Gideon","last_name":"Dresdner","first_name":"Gideon"},{"first_name":"Saurav","last_name":"Shekhar","full_name":"Shekhar, Saurav"},{"full_name":"Pedregosa, Fabian","first_name":"Fabian","last_name":"Pedregosa"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Rätsch, Gunnar","first_name":"Gunnar","last_name":"Rätsch"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"arxiv":1,"department":[{"_id":"FrLo"}],"publication":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","date_updated":"2023-09-11T11:14:30Z","page":"2337-2343","day":"19","quality_controlled":"1","publisher":"International Joint Conferences on Artificial Intelligence","date_published":"2021-05-19T00:00:00Z","external_id":{"arxiv":["2105.09240"]},"type":"conference","language":[{"iso":"eng"}],"extern":"1","month":"05","status":"public"},{"publication_identifier":{"isbn":["9781713845393"]},"main_file_link":[{"url":"https://arxiv.org/abs/2107.01057","open_access":"1"}],"oa_version":"Preprint","article_processing_charge":"No","publication_status":"published","title":"Backward-compatible prediction updates: A probabilistic approach","abstract":[{"text":"When machine learning systems meet real world applications, accuracy is only\r\none of several requirements. In this paper, we assay a complementary\r\nperspective originating from the increasing availability of pre-trained and\r\nregularly improving state-of-the-art models. While new improved models develop\r\nat a fast pace, downstream tasks vary more slowly or stay constant. Assume that\r\nwe have a large unlabelled data set for which we want to maintain accurate\r\npredictions. Whenever a new and presumably better ML models becomes available,\r\nwe encounter two problems: (i) given a limited budget, which data points should\r\nbe re-evaluated using the new model?; and (ii) if the new predictions differ\r\nfrom the current ones, should we update? Problem (i) is about compute cost,\r\nwhich matters for very large data sets and models. Problem (ii) is about\r\nmaintaining consistency of the predictions, which can be highly relevant for\r\ndownstream applications; our demand is to avoid negative flips, i.e., changing\r\ncorrect to incorrect predictions. In this paper, we formalize the Prediction\r\nUpdate Problem and present an efficient probabilistic approach as answer to the\r\nabove questions. In extensive experiments on standard classification benchmark\r\ndata sets, we show that our method outperforms alternative strategies along key\r\nmetrics for backward-compatible prediction updates.","lang":"eng"}],"citation":{"chicago":"Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:116–28, 2021.","short":"F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128.","apa":"Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf, B., &#38; Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic approach. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 116–128). Virtual.","mla":"Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, 2021, pp. 116–28.","ista":"Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 116–128.","ieee":"F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, 2021, vol. 34, pp. 116–128.","ama":"Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. Backward-compatible prediction updates: A probabilistic approach. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. ; 2021:116-128."},"year":"2021","date_created":"2023-08-22T14:05:41Z","_id":"14182","conference":{"end_date":"2021-12-10","location":"Virtual","name":"NeurIPS: Neural Information Processing Systems","start_date":"2021-12-07"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Frederik","last_name":"Träuble","full_name":"Träuble, Frederik"},{"first_name":"Julius von","last_name":"Kügelgen","full_name":"Kügelgen, Julius von"},{"full_name":"Kleindessner, Matthäus","last_name":"Kleindessner","first_name":"Matthäus"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"}],"volume":34,"oa":1,"intvolume":"        34","arxiv":1,"date_updated":"2023-09-11T11:31:59Z","publication":"35th Conference on Neural Information Processing Systems","department":[{"_id":"FrLo"}],"day":"02","page":"116-128","quality_controlled":"1","type":"conference","external_id":{"arxiv":["2107.01057"]},"date_published":"2021-07-02T00:00:00Z","extern":"1","language":[{"iso":"eng"}],"status":"public","month":"07"},{"_id":"14221","date_created":"2023-08-22T14:23:35Z","abstract":[{"text":"The world is structured in countless ways. It may be prudent to enforce corresponding structural properties to a learning algorithm's solution, such as incorporating prior beliefs, natural constraints, or causal structures. Doing so may translate to faster, more accurate, and more flexible models, which may directly relate to real-world impact. In this dissertation, we consider two different research areas that concern structuring a learning algorithm's solution: when the structure is known and when it has to be discovered.","lang":"eng"}],"article_number":"2111.13693","year":"2021","citation":{"mla":"Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” <i>ArXiv</i>, 2111.13693, doi:<a href=\"https://doi.org/10.48550/arXiv.2111.13693\">10.48550/arXiv.2111.13693</a>.","chicago":"Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2111.13693\">https://doi.org/10.48550/arXiv.2111.13693</a>.","short":"F. Locatello, ArXiv (n.d.).","apa":"Locatello, F. (n.d.). Enforcing and discovering structure in machine learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2111.13693\">https://doi.org/10.48550/arXiv.2111.13693</a>","ista":"Locatello F. Enforcing and discovering structure in machine learning. arXiv, 2111.13693.","ieee":"F. Locatello, “Enforcing and discovering structure in machine learning,” <i>arXiv</i>. .","ama":"Locatello F. Enforcing and discovering structure in machine learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2111.13693\">10.48550/arXiv.2111.13693</a>"},"article_processing_charge":"No","oa_version":"Preprint","publication_status":"submitted","title":"Enforcing and discovering structure in machine learning","day":"26","doi":"10.48550/arXiv.2111.13693","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2111.13693","open_access":"1"}],"status":"public","publication":"arXiv","date_updated":"2023-09-12T07:04:44Z","month":"11","department":[{"_id":"FrLo"}],"extern":"1","language":[{"iso":"eng"}],"arxiv":1,"oa":1,"external_id":{"arxiv":["2111.13693"]},"date_published":"2021-11-26T00:00:00Z","type":"preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"}]},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"full_name":"Dittadi, Andrea","last_name":"Dittadi","first_name":"Andrea"},{"first_name":"Manuel","last_name":"Wuthrich","full_name":"Wuthrich, Manuel"},{"full_name":"Widmaier, Felix","first_name":"Felix","last_name":"Widmaier"},{"first_name":"Peter Vincent","last_name":"Gehler","full_name":"Gehler, Peter Vincent"},{"last_name":"Winther","first_name":"Ole","full_name":"Winther, Ole"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"},{"first_name":"Olivier","last_name":"Bachem","full_name":"Bachem, Olivier"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"},{"full_name":"Bauer, Stefan","last_name":"Bauer","first_name":"Stefan"}],"type":"conference","date_published":"2021-07-23T00:00:00Z","extern":"1","language":[{"iso":"eng"}],"date_updated":"2023-09-13T12:44:00Z","status":"public","publication":"ICML 2021 Workshop on Unsupervised Reinforcement Learning","department":[{"_id":"FrLo"}],"month":"07","day":"23","article_processing_charge":"No","oa_version":"None","quality_controlled":"1","title":"Representation learning for out-of-distribution generalization in reinforcement learning","publication_status":"published","abstract":[{"lang":"eng","text":"Learning data representations that are useful for various downstream tasks is a cornerstone of artificial intelligence. While existing methods are typically evaluated on downstream tasks such as classification or generative image quality, we propose to assess representations through their usefulness in downstream control tasks, such as reaching or pushing objects. By training over 10,000 reinforcement learning policies, we extensively evaluate to what extent different representation properties affect out-of-distribution (OOD) generalization. Finally, we demonstrate zero-shot transfer of these policies from simulation to the real world, without any domain randomization or fine-tuning. This paper aims to establish the first systematic characterization of the usefulness of learned representations for real-world OOD downstream tasks."}],"citation":{"ama":"Träuble F, Dittadi A, Wuthrich M, et al. Representation learning for out-of-distribution generalization in reinforcement learning. In: <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>. ; 2021.","ista":"Träuble F, Dittadi A, Wuthrich M, Widmaier F, Gehler PV, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2021. Representation learning for out-of-distribution generalization in reinforcement learning. ICML 2021 Workshop on Unsupervised Reinforcement Learning. ICML: International Conference on Machine Learning.","ieee":"F. Träuble <i>et al.</i>, “Representation learning for out-of-distribution generalization in reinforcement learning,” in <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>, Virtual, 2021.","chicago":"Träuble, Frederik, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” In <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>, 2021.","short":"F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.","apa":"Träuble, F., Dittadi, A., Wuthrich, M., Widmaier, F., Gehler, P. V., Winther, O., … Bauer, S. (2021). Representation learning for out-of-distribution generalization in reinforcement learning. In <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>. Virtual.","mla":"Träuble, Frederik, et al. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>, 2021."},"year":"2021","date_created":"2023-09-13T12:43:14Z","_id":"14332","conference":{"end_date":"2021-07-23","location":"Virtual","name":"ICML: International Conference on Machine Learning","start_date":"2021-07-23"}},{"title":"Increased susceptibility and intrinsic apoptotic signaling in neurons by induced HDAC3 expression","publication_status":"published","article_processing_charge":"Yes","oa_version":"Published Version","file_date_updated":"2022-05-13T07:40:15Z","scopus_import":"1","doi":"10.1167/IOVS.62.10.14","publication_identifier":{"issn":["0146-0404"],"eissn":["1552-5783"]},"acknowledgement":"The authors thank Joel Dietz for maintaining the mice used in this study, Satoshi Kinoshita and the Translational Research Initiative in Pathology Laboratory at the University of Wisconsin-Madison for cutting retinal sections analyzed in this study, and Mark Banghart for statistical review of the data analysis. Supported by National Eye Institute Grants R01 EY012223 (RWN), R01 EY030123 (RWN), R01 EY029809 (LWG), R01 EY029809 (LWG) and a Vision Research CORE grant P30 EY016665, NRSA grant T32 GM081061, by an unrestricted research grant from Research to Prevent Blindness, Inc., and by a University of Wisconsin-Madison Vilas Life Cycle award and the Frederick A. Davis Research Chair (RWN). ","article_type":"original","date_created":"2021-09-12T22:01:23Z","file":[{"checksum":"c430967746f653aa1ae84ee617f62b73","content_type":"application/pdf","file_id":"11369","creator":"dernst","relation":"main_file","date_created":"2022-05-13T07:40:15Z","date_updated":"2022-05-13T07:40:15Z","file_size":19707796,"file_name":"2021_IOVS_Schmitt.pdf","access_level":"open_access","success":1}],"_id":"10000","citation":{"mla":"Schmitt, Heather M., et al. “Increased Susceptibility and Intrinsic Apoptotic Signaling in Neurons by Induced HDAC3 Expression.” <i>Investigative Ophthalmology and Visual Science</i>, vol. 62, no. 10, 14, Association for Research in Vision and Ophthalmology, 2021, doi:<a href=\"https://doi.org/10.1167/IOVS.62.10.14\">10.1167/IOVS.62.10.14</a>.","chicago":"Schmitt, Heather M., Rachel L. Fehrman, Margaret E Maes, Huan Yang, Lian Wang Guo, Cassandra L. Schlamp, Heather R. Pelzel, and Robert W. Nickells. “Increased Susceptibility and Intrinsic Apoptotic Signaling in Neurons by Induced HDAC3 Expression.” <i>Investigative Ophthalmology and Visual Science</i>. Association for Research in Vision and Ophthalmology, 2021. <a href=\"https://doi.org/10.1167/IOVS.62.10.14\">https://doi.org/10.1167/IOVS.62.10.14</a>.","apa":"Schmitt, H. M., Fehrman, R. L., Maes, M. E., Yang, H., Guo, L. W., Schlamp, C. L., … Nickells, R. W. (2021). Increased susceptibility and intrinsic apoptotic signaling in neurons by induced HDAC3 expression. <i>Investigative Ophthalmology and Visual Science</i>. Association for Research in Vision and Ophthalmology. <a href=\"https://doi.org/10.1167/IOVS.62.10.14\">https://doi.org/10.1167/IOVS.62.10.14</a>","short":"H.M. Schmitt, R.L. Fehrman, M.E. Maes, H. Yang, L.W. Guo, C.L. Schlamp, H.R. Pelzel, R.W. Nickells, Investigative Ophthalmology and Visual Science 62 (2021).","ieee":"H. M. Schmitt <i>et al.</i>, “Increased susceptibility and intrinsic apoptotic signaling in neurons by induced HDAC3 expression,” <i>Investigative Ophthalmology and Visual Science</i>, vol. 62, no. 10. Association for Research in Vision and Ophthalmology, 2021.","ista":"Schmitt HM, Fehrman RL, Maes ME, Yang H, Guo LW, Schlamp CL, Pelzel HR, Nickells RW. 2021. Increased susceptibility and intrinsic apoptotic signaling in neurons by induced HDAC3 expression. Investigative Ophthalmology and Visual Science. 62(10), 14.","ama":"Schmitt HM, Fehrman RL, Maes ME, et al. Increased susceptibility and intrinsic apoptotic signaling in neurons by induced HDAC3 expression. <i>Investigative Ophthalmology and Visual Science</i>. 2021;62(10). doi:<a href=\"https://doi.org/10.1167/IOVS.62.10.14\">10.1167/IOVS.62.10.14</a>"},"year":"2021","article_number":"14","abstract":[{"lang":"eng","text":"Inhibition or targeted deletion of histone deacetylase 3 (HDAC3) is neuroprotective in a variety neurodegenerative conditions, including retinal ganglion cells (RGCs) after acute optic nerve damage. Consistent with this, induced HDAC3 expression in cultured cells shows selective toxicity to neurons. Despite an established role for HDAC3 in neuronal pathology, little is known regarding the mechanism of this pathology."}],"volume":62,"oa":1,"author":[{"full_name":"Schmitt, Heather M.","last_name":"Schmitt","first_name":"Heather M."},{"full_name":"Fehrman, Rachel L.","last_name":"Fehrman","first_name":"Rachel L."},{"first_name":"Margaret E","last_name":"Maes","id":"3838F452-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9642-1085","full_name":"Maes, Margaret E"},{"full_name":"Yang, Huan","last_name":"Yang","first_name":"Huan"},{"full_name":"Guo, Lian Wang","first_name":"Lian Wang","last_name":"Guo"},{"last_name":"Schlamp","first_name":"Cassandra L.","full_name":"Schlamp, Cassandra L."},{"full_name":"Pelzel, Heather R.","last_name":"Pelzel","first_name":"Heather R."},{"first_name":"Robert W.","last_name":"Nickells","full_name":"Nickells, Robert W."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","department":[{"_id":"SaSi"}],"date_updated":"2023-08-14T06:35:17Z","publication":"Investigative Ophthalmology and Visual Science","intvolume":"        62","quality_controlled":"1","day":"16","issue":"10","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","has_accepted_license":"1","tmp":{"image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"type":"journal_article","date_published":"2021-08-16T00:00:00Z","external_id":{"isi":["000695230000014"],"pmid":["34398198"]},"pmid":1,"publisher":"Association for Research in Vision and Ophthalmology","isi":1,"month":"08","status":"public","ddc":["570"],"language":[{"iso":"eng"}]},{"external_id":{"isi":["000947350400089"],"arxiv":["2104.07466"]},"date_published":"2021-07-07T00:00:00Z","type":"conference","publisher":"Institute of Electrical and Electronics Engineers","status":"public","month":"07","isi":1,"keyword":["Computer science","Computational modeling","Markov processes","Probabilistic logic","Formal verification","Game Theory"],"language":[{"iso":"eng"}],"quality_controlled":"1","day":"07","page":"1-13","project":[{"call_identifier":"FWF","grant_number":"S11407","name":"Game Theory","_id":"25863FF4-B435-11E9-9278-68D0E5697425"},{"name":"Formal Methods for Stochastic Models: Algorithms and Applications","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","grant_number":"863818","call_identifier":"H2020"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","author":[{"full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X","last_name":"Chatterjee","first_name":"Krishnendu"},{"full_name":"Dvorak, Wolfgang","last_name":"Dvorak","first_name":"Wolfgang"},{"orcid":"0000-0002-5008-6530","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","full_name":"Henzinger, Monika H","first_name":"Monika H","last_name":"Henzinger"},{"full_name":"Svozil, Alexander","last_name":"Svozil","first_name":"Alexander"}],"publication":"Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science","date_updated":"2025-07-14T09:10:07Z","ec_funded":1,"department":[{"_id":"KrCh"}],"arxiv":1,"oa_version":"Preprint","article_processing_charge":"No","title":"Symbolic time and space tradeoffs for probabilistic verification","publication_status":"published","publication_identifier":{"issn":["1043-6871"],"eisbn":["978-1-6654-4895-6"],"isbn":["978-1-6654-4896-3"]},"acknowledgement":"The authors are grateful to the anonymous referees for their valuable comments. A. S. is fully supported by the Vienna Science and Technology Fund (WWTF) through project ICT15–003. K. C. is supported by the Austrian Science Fund (FWF) NFN Grant No S11407-N23 (RiSE/SHiNE) and by the ERC CoG 863818 (ForM-SMArt). For M. H. the research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP/2007–2013) / ERC Grant Agreement no. 340506.","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2104.07466"}],"doi":"10.1109/LICS52264.2021.9470739","scopus_import":"1","_id":"10002","date_created":"2021-09-12T22:01:24Z","conference":{"name":"LICS: Symposium on Logic in Computer Science","location":"Rome, Italy","end_date":"2021-07-02","start_date":"2021-06-29"},"abstract":[{"lang":"eng","text":"We present a faster symbolic algorithm for the following central problem in probabilistic verification: Compute the maximal end-component (MEC) decomposition of Markov decision processes (MDPs). This problem generalizes the SCC decomposition problem of graphs and closed recurrent sets of Markov chains. The model of symbolic algorithms is widely used in formal verification and model-checking, where access to the input model is restricted to only symbolic operations (e.g., basic set operations and computation of one-step neighborhood). For an input MDP with  n  vertices and  m  edges, the classical symbolic algorithm from the 1990s for the MEC decomposition requires  O(n2)  symbolic operations and  O(1)  symbolic space. The only other symbolic algorithm for the MEC decomposition requires  O(nm−−√)  symbolic operations and  O(m−−√)  symbolic space. A main open question is whether the worst-case  O(n2)  bound for symbolic operations can be beaten. We present a symbolic algorithm that requires  O˜(n1.5)  symbolic operations and  O˜(n−−√)  symbolic space. Moreover, the parametrization of our algorithm provides a trade-off between symbolic operations and symbolic space: for all  0<ϵ≤1/2  the symbolic algorithm requires  O˜(n2−ϵ)  symbolic operations and  O˜(nϵ)  symbolic space ( O˜  hides poly-logarithmic factors). Using our techniques we present faster algorithms for computing the almost-sure winning regions of  ω -regular objectives for MDPs. We consider the canonical parity objectives for  ω -regular objectives, and for parity objectives with  d -priorities we present an algorithm that computes the almost-sure winning region with  O˜(n2−ϵ)  symbolic operations and  O˜(nϵ)  symbolic space, for all  0<ϵ≤1/2 ."}],"year":"2021","citation":{"short":"K. Chatterjee, W. Dvorak, M.H. Henzinger, A. Svozil, in:, Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science, Institute of Electrical and Electronics Engineers, 2021, pp. 1–13.","apa":"Chatterjee, K., Dvorak, W., Henzinger, M. H., &#38; Svozil, A. (2021). Symbolic time and space tradeoffs for probabilistic verification. In <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i> (pp. 1–13). Rome, Italy: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/LICS52264.2021.9470739\">https://doi.org/10.1109/LICS52264.2021.9470739</a>","chicago":"Chatterjee, Krishnendu, Wolfgang Dvorak, Monika H Henzinger, and Alexander Svozil. “Symbolic Time and Space Tradeoffs for Probabilistic Verification.” In <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, 1–13. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/LICS52264.2021.9470739\">https://doi.org/10.1109/LICS52264.2021.9470739</a>.","mla":"Chatterjee, Krishnendu, et al. “Symbolic Time and Space Tradeoffs for Probabilistic Verification.” <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, Institute of Electrical and Electronics Engineers, 2021, pp. 1–13, doi:<a href=\"https://doi.org/10.1109/LICS52264.2021.9470739\">10.1109/LICS52264.2021.9470739</a>.","ama":"Chatterjee K, Dvorak W, Henzinger MH, Svozil A. Symbolic time and space tradeoffs for probabilistic verification. In: <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>. Institute of Electrical and Electronics Engineers; 2021:1-13. doi:<a href=\"https://doi.org/10.1109/LICS52264.2021.9470739\">10.1109/LICS52264.2021.9470739</a>","ieee":"K. Chatterjee, W. Dvorak, M. H. Henzinger, and A. Svozil, “Symbolic time and space tradeoffs for probabilistic verification,” in <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, Rome, Italy, 2021, pp. 1–13.","ista":"Chatterjee K, Dvorak W, Henzinger MH, Svozil A. 2021. Symbolic time and space tradeoffs for probabilistic verification. Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science. LICS: Symposium on Logic in Computer Science, 1–13."}},{"arxiv":1,"department":[{"_id":"KrCh"}],"date_updated":"2025-07-14T09:10:08Z","ec_funded":1,"publication":"Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science","author":[{"full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee","first_name":"Krishnendu"},{"last_name":"Doyen","first_name":"Laurent","full_name":"Doyen, Laurent"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"citation":{"mla":"Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected Stopping Time.” <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, Institute of Electrical and Electronics Engineers, 2021, pp. 1–13, doi:<a href=\"https://doi.org/10.1109/LICS52264.2021.9470595\">10.1109/LICS52264.2021.9470595</a>.","apa":"Chatterjee, K., &#38; Doyen, L. (2021). Stochastic processes with expected stopping time. In <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i> (pp. 1–13). Rome, Italy: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/LICS52264.2021.9470595\">https://doi.org/10.1109/LICS52264.2021.9470595</a>","short":"K. Chatterjee, L. Doyen, in:, Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science, Institute of Electrical and Electronics Engineers, 2021, pp. 1–13.","chicago":"Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected Stopping Time.” In <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, 1–13. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/LICS52264.2021.9470595\">https://doi.org/10.1109/LICS52264.2021.9470595</a>.","ama":"Chatterjee K, Doyen L. Stochastic processes with expected stopping time. In: <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>. Institute of Electrical and Electronics Engineers; 2021:1-13. doi:<a href=\"https://doi.org/10.1109/LICS52264.2021.9470595\">10.1109/LICS52264.2021.9470595</a>","ista":"Chatterjee K, Doyen L. 2021. Stochastic processes with expected stopping time. Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science. LICS: Symposium on Logic in Computer Science, 1–13.","ieee":"K. Chatterjee and L. Doyen, “Stochastic processes with expected stopping time,” in <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, Rome, Italy, 2021, pp. 1–13."},"year":"2021","abstract":[{"lang":"eng","text":"Markov chains are the de facto finite-state model for stochastic dynamical systems, and Markov decision processes (MDPs) extend Markov chains by incorporating non-deterministic behaviors. Given an MDP and rewards on states, a classical optimization criterion is the maximal expected total reward where the MDP stops after T steps, which can be computed by a simple dynamic programming algorithm. We consider a natural generalization of the problem where the stopping times can be chosen according to a probability distribution, such that the expected stopping time is T, to optimize the expected total reward. Quite surprisingly we establish inter-reducibility of the expected stopping-time problem for Markov chains with the Positivity problem (which is related to the well-known Skolem problem), for which establishing either decidability or undecidability would be a major breakthrough. Given the hardness of the exact problem, we consider the approximate version of the problem: we show that it can be solved in exponential time for Markov chains and in exponential space for MDPs."}],"conference":{"end_date":"2021-07-02","location":"Rome, Italy","name":"LICS: Symposium on Logic in Computer Science","start_date":"2021-06-29"},"date_created":"2021-09-12T22:01:25Z","_id":"10004","scopus_import":"1","doi":"10.1109/LICS52264.2021.9470595","main_file_link":[{"url":"https://arxiv.org/abs/2104.07278","open_access":"1"}],"acknowledgement":"We are grateful to the anonymous reviewers of LICS 2021 and of a previous version of this paper for insightful comments that helped improving the presentation. This research was partially supported by the grant ERC CoG 863818 (ForM-SMArt).","publication_identifier":{"isbn":["978-1-6654-4896-3"],"eisbn":["978-1-6654-4895-6"],"issn":["1043-6871"]},"publication_status":"published","title":"Stochastic processes with expected stopping time","article_processing_charge":"No","oa_version":"Preprint","language":[{"iso":"eng"}],"keyword":["Computer science","Heuristic algorithms","Memory management","Automata","Markov processes","Probability distribution","Complexity theory"],"isi":1,"month":"07","status":"public","publisher":"Institute of Electrical and Electronics Engineers","type":"conference","external_id":{"arxiv":["2104.07278"],"isi":["000947350400036"]},"date_published":"2021-07-07T00:00:00Z","project":[{"call_identifier":"H2020","grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E"}],"page":"1-13","day":"07","quality_controlled":"1"},{"publisher":"World Scientific","external_id":{"arxiv":["2009.06917"],"isi":["000722222900004"]},"date_published":"2021-08-25T00:00:00Z","type":"journal_article","keyword":["Nonlinear parabolic systems","implicit constitutive theory","weak solutions","existence","uniqueness"],"language":[{"iso":"eng"}],"status":"public","month":"08","isi":1,"day":"25","quality_controlled":"1","project":[{"grant_number":"F6504","name":"Taming Complexity in Partial Differential Systems","_id":"fc31cba2-9c52-11eb-aca3-ff467d239cd2"}],"issue":"09","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Bulíček, Miroslav","first_name":"Miroslav","last_name":"Bulíček"},{"id":"dbabca31-66eb-11eb-963a-fb9c22c880b4","full_name":"Maringová, Erika","first_name":"Erika","last_name":"Maringová"},{"full_name":"Málek, Josef","last_name":"Málek","first_name":"Josef"}],"oa":1,"volume":31,"intvolume":"        31","arxiv":1,"publication":"Mathematical Models and Methods in Applied Sciences","date_updated":"2023-09-04T11:43:45Z","department":[{"_id":"JuFi"}],"article_type":"original","publication_identifier":{"issn":["0218-2025"],"eissn":["1793-6314"]},"acknowledgement":"M. Bulíček and J. Málek acknowledge the support of the project No. 18-12719S financed by the Czech\r\nScience foundation (GAČR). E. Maringová acknowledges support from Charles University Research program \r\nUNCE/SCI/023, the grant SVV-2020-260583 by the Ministry of Education, Youth and Sports, Czech Republic\r\nand from the Austrian Science Fund (FWF), grants P30000, W1245, and F65. M. Bulíček and J. Málek are\r\nmembers of the Nečas Center for Mathematical Modelling.\r\n","doi":"10.1142/S0218202521500457","main_file_link":[{"url":"https://arxiv.org/abs/2009.06917","open_access":"1"}],"scopus_import":"1","article_processing_charge":"No","oa_version":"Preprint","publication_status":"published","title":"On nonlinear problems of parabolic type with implicit constitutive equations involving flux","abstract":[{"lang":"eng","text":"We study systems of nonlinear partial differential equations of parabolic type, in which the elliptic operator is replaced by the first-order divergence operator acting on a flux function, which is related to the spatial gradient of the unknown through an additional implicit equation. This setting, broad enough in terms of applications, significantly expands the paradigm of nonlinear parabolic problems. Formulating four conditions concerning the form of the implicit equation, we first show that these conditions describe a maximal monotone p-coercive graph. We then establish the global-in-time and large-data existence of a (weak) solution and its uniqueness. To this end, we adopt and significantly generalize Minty’s method of monotone mappings. A unified theory, containing several novel tools, is developed in a way to be tractable from the point of view of numerical approximations."}],"year":"2021","citation":{"ama":"Bulíček M, Maringová E, Málek J. On nonlinear problems of parabolic type with implicit constitutive equations involving flux. <i>Mathematical Models and Methods in Applied Sciences</i>. 2021;31(09). doi:<a href=\"https://doi.org/10.1142/S0218202521500457\">10.1142/S0218202521500457</a>","ista":"Bulíček M, Maringová E, Málek J. 2021. On nonlinear problems of parabolic type with implicit constitutive equations involving flux. Mathematical Models and Methods in Applied Sciences. 31(09).","ieee":"M. Bulíček, E. Maringová, and J. Málek, “On nonlinear problems of parabolic type with implicit constitutive equations involving flux,” <i>Mathematical Models and Methods in Applied Sciences</i>, vol. 31, no. 09. World Scientific, 2021.","apa":"Bulíček, M., Maringová, E., &#38; Málek, J. (2021). On nonlinear problems of parabolic type with implicit constitutive equations involving flux. <i>Mathematical Models and Methods in Applied Sciences</i>. World Scientific. <a href=\"https://doi.org/10.1142/S0218202521500457\">https://doi.org/10.1142/S0218202521500457</a>","short":"M. Bulíček, E. Maringová, J. Málek, Mathematical Models and Methods in Applied Sciences 31 (2021).","chicago":"Bulíček, Miroslav, Erika Maringová, and Josef Málek. “On Nonlinear Problems of Parabolic Type with Implicit Constitutive Equations Involving Flux.” <i>Mathematical Models and Methods in Applied Sciences</i>. World Scientific, 2021. <a href=\"https://doi.org/10.1142/S0218202521500457\">https://doi.org/10.1142/S0218202521500457</a>.","mla":"Bulíček, Miroslav, et al. “On Nonlinear Problems of Parabolic Type with Implicit Constitutive Equations Involving Flux.” <i>Mathematical Models and Methods in Applied Sciences</i>, vol. 31, no. 09, World Scientific, 2021, doi:<a href=\"https://doi.org/10.1142/S0218202521500457\">10.1142/S0218202521500457</a>."},"_id":"10005","date_created":"2021-09-12T22:01:25Z"}]