[{"month":"07","language":[{"iso":"eng"}],"date_published":"2023-07-01T00:00:00Z","arxiv":1,"department":[{"_id":"MaMo"}],"publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","date_updated":"2025-09-08T08:31:53Z","oa":1,"file_date_updated":"2023-08-21T07:23:18Z","_id":"14083","scopus_import":"1","intvolume":"       261","oa_version":"Published Version","type":"conference","acknowledgement":"Nicolas Resch: Research supported in part by ERC H2020 grant No.74079 (ALGSTRONGCRYPTO). Chen Yuan: Research supported in part by the National Key Research and Development Projects under Grant 2022YFA1004900 and Grant 2021YFE0109900, the National Natural Science Foundation of China under Grant 12101403 and Grant 12031011.\r\nAcknowledgements YZ is grateful to Shashank Vatedka, Diyuan Wu and Fengxing Zhu for inspiring discussions.","citation":{"ista":"Resch N, Yuan C, Zhang Y. 2023. Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery. 50th International Colloquium on Automata, Languages, and Programming. ICALP: Automata, Languages and Programming, LIPIcs, vol. 261, 99.","ama":"Resch N, Yuan C, Zhang Y. Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery. In: <i>50th International Colloquium on Automata, Languages, and Programming</i>. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.99\">10.4230/LIPIcs.ICALP.2023.99</a>","short":"N. Resch, C. Yuan, Y. Zhang, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.","ieee":"N. Resch, C. Yuan, and Y. Zhang, “Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery,” in <i>50th International Colloquium on Automata, Languages, and Programming</i>, Paderborn, Germany, 2023, vol. 261.","mla":"Resch, Nicolas, et al. “Zero-Rate Thresholds and New Capacity Bounds for List-Decoding and List-Recovery.” <i>50th International Colloquium on Automata, Languages, and Programming</i>, vol. 261, 99, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.99\">10.4230/LIPIcs.ICALP.2023.99</a>.","apa":"Resch, N., Yuan, C., &#38; Zhang, Y. (2023). Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery. In <i>50th International Colloquium on Automata, Languages, and Programming</i> (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.99\">https://doi.org/10.4230/LIPIcs.ICALP.2023.99</a>","chicago":"Resch, Nicolas, Chen Yuan, and Yihan Zhang. “Zero-Rate Thresholds and New Capacity Bounds for List-Decoding and List-Recovery.” In <i>50th International Colloquium on Automata, Languages, and Programming</i>, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.99\">https://doi.org/10.4230/LIPIcs.ICALP.2023.99</a>."},"alternative_title":["LIPIcs"],"day":"01","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"title":"Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery","article_processing_charge":"Yes","abstract":[{"lang":"eng","text":"In this work we consider the list-decodability and list-recoverability of arbitrary q-ary codes, for all integer values of q ≥ 2. A code is called (p,L)_q-list-decodable if every radius pn Hamming ball contains less than L codewords; (p,𝓁,L)_q-list-recoverability is a generalization where we place radius pn Hamming balls on every point of a combinatorial rectangle with side length 𝓁 and again stipulate that there be less than L codewords.\r\nOur main contribution is to precisely calculate the maximum value of p for which there exist infinite families of positive rate (p,𝓁,L)_q-list-recoverable codes, the quantity we call the zero-rate threshold. Denoting this value by p_*, we in fact show that codes correcting a p_*+ε fraction of errors must have size O_ε(1), i.e., independent of n. Such a result is typically referred to as a \"Plotkin bound.\" To complement this, a standard random code with expurgation construction shows that there exist positive rate codes correcting a p_*-ε fraction of errors. We also follow a classical proof template (typically attributed to Elias and Bassalygo) to derive from the zero-rate threshold other tradeoffs between rate and decoding radius for list-decoding and list-recovery.\r\nTechnically, proving the Plotkin bound boils down to demonstrating the Schur convexity of a certain function defined on the q-simplex as well as the convexity of a univariate function derived from it. We remark that an earlier argument claimed similar results for q-ary list-decoding; however, we point out that this earlier proof is flawed."}],"publication_status":"published","external_id":{"arxiv":["2210.07754"]},"related_material":{"record":[{"relation":"later_version","id":"17330","status":"public"}]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Nicolas","last_name":"Resch","full_name":"Resch, Nicolas"},{"first_name":"Chen","last_name":"Yuan","full_name":"Yuan, Chen"},{"id":"2ce5da42-b2ea-11eb-bba5-9f264e9d002c","orcid":"0000-0002-6465-6258","full_name":"Zhang, Yihan","last_name":"Zhang","first_name":"Yihan"}],"volume":261,"has_accepted_license":"1","conference":{"end_date":"2023-07-14","location":"Paderborn, Germany","name":"ICALP: Automata, Languages and Programming","start_date":"2023-07-10"},"ddc":["000"],"corr_author":"1","quality_controlled":"1","file":[{"file_id":"14091","access_level":"open_access","relation":"main_file","checksum":"a449143fec3fbebb092cb8ef3b53c226","date_updated":"2023-08-21T07:23:18Z","file_size":1141497,"date_created":"2023-08-21T07:23:18Z","content_type":"application/pdf","file_name":"2023_LIPIcsICALP_Resch.pdf","success":1,"creator":"dernst"}],"doi":"10.4230/LIPIcs.ICALP.2023.99","publication_identifier":{"isbn":["9783959772785"],"issn":["1868-8969"]},"publication":"50th International Colloquium on Automata, Languages, and Programming","year":"2023","date_created":"2023-08-20T22:01:13Z","article_number":"99"},{"quality_controlled":"1","file":[{"date_updated":"2023-08-21T06:45:16Z","file_size":917791,"access_level":"open_access","relation":"main_file","checksum":"6dee0684245bb1c524b9c955db1e933d","file_id":"14088","success":1,"creator":"dernst","content_type":"application/pdf","file_name":"2023_LIPIcsICALP_Harris.pdf","date_created":"2023-08-21T06:45:16Z"}],"corr_author":"1","ddc":["000","510"],"conference":{"location":"Paderborn, Germany","end_date":"2023-07-14","start_date":"2023-07-10","name":"ICALP: Automata, Languages and Programming"},"has_accepted_license":"1","article_number":"72","date_created":"2023-08-20T22:01:14Z","year":"2023","publication":"50th International Colloquium on Automata, Languages, and Programming","doi":"10.4230/LIPIcs.ICALP.2023.72","publication_identifier":{"isbn":["9783959772785"],"issn":["1868-8969"]},"external_id":{"arxiv":["2007.10824"]},"publication_status":"published","abstract":[{"lang":"eng","text":"A central problem in computational statistics is to convert a procedure for sampling combinatorial objects into a procedure for counting those objects, and vice versa. We will consider sampling problems which come from Gibbs distributions, which are families of probability distributions over a discrete space Ω with probability mass function of the form μ^Ω_β(ω) ∝ e^{β H(ω)} for β in an interval [β_min, β_max] and H(ω) ∈ {0} ∪ [1, n].\r\nThe partition function is the normalization factor Z(β) = ∑_{ω ∈ Ω} e^{β H(ω)}, and the log partition ratio is defined as q = (log Z(β_max))/Z(β_min)\r\nWe develop a number of algorithms to estimate the counts c_x using roughly Õ(q/ε²) samples for general Gibbs distributions and Õ(n²/ε²) samples for integer-valued distributions (ignoring some second-order terms and parameters), We show this is optimal up to logarithmic factors. We illustrate with improved algorithms for counting connected subgraphs and perfect matchings in a graph."}],"article_processing_charge":"Yes","title":"Parameter estimation for Gibbs distributions","volume":261,"author":[{"first_name":"David G.","last_name":"Harris","full_name":"Harris, David G."},{"first_name":"Vladimir","last_name":"Kolmogorov","id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","full_name":"Kolmogorov, Vladimir"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"status":"public","id":"18855","relation":"later_version"}]},"citation":{"apa":"Harris, D. G., &#38; Kolmogorov, V. (2023). Parameter estimation for Gibbs distributions. In <i>50th International Colloquium on Automata, Languages, and Programming</i> (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.72\">https://doi.org/10.4230/LIPIcs.ICALP.2023.72</a>","chicago":"Harris, David G., and Vladimir Kolmogorov. “Parameter Estimation for Gibbs Distributions.” In <i>50th International Colloquium on Automata, Languages, and Programming</i>, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.72\">https://doi.org/10.4230/LIPIcs.ICALP.2023.72</a>.","mla":"Harris, David G., and Vladimir Kolmogorov. “Parameter Estimation for Gibbs Distributions.” <i>50th International Colloquium on Automata, Languages, and Programming</i>, vol. 261, 72, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.72\">10.4230/LIPIcs.ICALP.2023.72</a>.","ieee":"D. G. Harris and V. Kolmogorov, “Parameter estimation for Gibbs distributions,” in <i>50th International Colloquium on Automata, Languages, and Programming</i>, Paderborn, Germany, 2023, vol. 261.","short":"D.G. Harris, V. Kolmogorov, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.","ama":"Harris DG, Kolmogorov V. Parameter estimation for Gibbs distributions. In: <i>50th International Colloquium on Automata, Languages, and Programming</i>. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.72\">10.4230/LIPIcs.ICALP.2023.72</a>","ista":"Harris DG, Kolmogorov V. 2023. Parameter estimation for Gibbs distributions. 50th International Colloquium on Automata, Languages, and Programming. ICALP: Automata, Languages and Programming, LIPIcs, vol. 261, 72."},"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"day":"01","status":"public","alternative_title":["LIPIcs"],"file_date_updated":"2023-08-21T06:45:16Z","oa":1,"date_updated":"2025-07-10T11:50:45Z","arxiv":1,"department":[{"_id":"VlKo"}],"publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","date_published":"2023-07-01T00:00:00Z","language":[{"iso":"eng"}],"month":"07","acknowledgement":"We thank Heng Guo for helpful explanations of algorithms for sampling connected subgraphs and matchings, Maksym Serbyn for bringing to our attention the Wang-Landau algorithm and its use in physics.","type":"conference","intvolume":"       261","oa_version":"Published Version","scopus_import":"1","_id":"14084"},{"project":[{"_id":"bd9ca328-d553-11ed-ba76-dc4f890cfe62","grant_number":"101019564","call_identifier":"H2020","name":"The design and evaluation of modern fully dynamic data structures"},{"name":"Static and Dynamic Hierarchical Graph Decompositions","_id":"bda196b2-d553-11ed-ba76-8e8ee6c21103","grant_number":"I05982"},{"name":"Fast Algorithms for a Reactive Network Layer","_id":"bd9e3a2e-d553-11ed-ba76-8aa684ce17fe","grant_number":"P33775"}],"citation":{"ista":"Goranci G, Henzinger M. 2023. Efficient data structures for incremental exact and approximate maximum flow. 50th International Colloquium on Automata, Languages, and Programming. ICALP: Automata, Languages and Programming, LIPIcs, vol. 261, 69.","ama":"Goranci G, Henzinger M. Efficient data structures for incremental exact and approximate maximum flow. In: <i>50th International Colloquium on Automata, Languages, and Programming</i>. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.69\">10.4230/LIPIcs.ICALP.2023.69</a>","short":"G. Goranci, M. Henzinger, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.","ieee":"G. Goranci and M. Henzinger, “Efficient data structures for incremental exact and approximate maximum flow,” in <i>50th International Colloquium on Automata, Languages, and Programming</i>, Paderborn, Germany, 2023, vol. 261.","mla":"Goranci, Gramoz, and Monika Henzinger. “Efficient Data Structures for Incremental Exact and Approximate Maximum Flow.” <i>50th International Colloquium on Automata, Languages, and Programming</i>, vol. 261, 69, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.69\">10.4230/LIPIcs.ICALP.2023.69</a>.","apa":"Goranci, G., &#38; Henzinger, M. (2023). Efficient data structures for incremental exact and approximate maximum flow. In <i>50th International Colloquium on Automata, Languages, and Programming</i> (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.69\">https://doi.org/10.4230/LIPIcs.ICALP.2023.69</a>","chicago":"Goranci, Gramoz, and Monika Henzinger. “Efficient Data Structures for Incremental Exact and Approximate Maximum Flow.” In <i>50th International Colloquium on Automata, Languages, and Programming</i>, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.69\">https://doi.org/10.4230/LIPIcs.ICALP.2023.69</a>."},"status":"public","day":"01","alternative_title":["LIPIcs"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ec_funded":1,"date_updated":"2025-06-04T07:19:37Z","publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","arxiv":1,"department":[{"_id":"MoHe"}],"date_published":"2023-07-01T00:00:00Z","language":[{"iso":"eng"}],"month":"07","file_date_updated":"2023-08-21T06:59:05Z","oa":1,"scopus_import":"1","_id":"14085","acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No.\r\n101019564 “The Design of Modern Fully Dynamic Data Structures (MoDynStruct)” and from the\r\nAustrian Science Fund (FWF) project “Static and Dynamic Hierarchical Graph Decompositions”,\r\nI 5982-N, and project “Fast Algorithms for a Reactive Network Layer (ReactNet)”, P 33775-N, with additional funding from the netidee SCIENCE Stiftung, 2020–2024.\r\nThis work was done in part while Gramoz Goranci was at Institute for Theoretical Studies, ETH Zurich, Switzerland. There, he was supported by Dr. Max Rössler, the Walter Haefner Foundation and the ETH Zürich Foundation. We also thank Richard Peng, Thatchaphol Saranurak, Sebastian Forster and Sushant Sachdeva for helpful discussions, and the anonymous reviewers for their insightful comments.","type":"conference","oa_version":"Published Version","intvolume":"       261","ddc":["000"],"conference":{"start_date":"2023-07-10","name":"ICALP: Automata, Languages and Programming","end_date":"2023-07-14","location":"Paderborn, Germany"},"has_accepted_license":"1","file":[{"file_id":"14089","relation":"main_file","checksum":"074177e815a1656de5d4071c7a3dffa6","access_level":"open_access","file_size":875910,"date_updated":"2023-08-21T06:59:05Z","date_created":"2023-08-21T06:59:05Z","content_type":"application/pdf","file_name":"2023_LIPIcsICALP_Goranci.pdf","creator":"dernst","success":1}],"quality_controlled":"1","corr_author":"1","publication":"50th International Colloquium on Automata, Languages, and Programming","doi":"10.4230/LIPIcs.ICALP.2023.69","publication_identifier":{"issn":["1868-8969"],"isbn":["9783959772785"]},"article_number":"69","date_created":"2023-08-20T22:01:14Z","year":"2023","title":"Efficient data structures for incremental exact and approximate maximum flow","external_id":{"arxiv":["2211.09606"]},"publication_status":"published","abstract":[{"lang":"eng","text":"We show an (1+ϵ)-approximation algorithm for maintaining maximum s-t flow under m edge insertions in m1/2+o(1)ϵ−1/2 amortized update time for directed, unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm in general sparse graphs with arbitrarily good approximation guarantee."}],"article_processing_charge":"Yes","volume":261,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Goranci, Gramoz","first_name":"Gramoz","last_name":"Goranci"},{"orcid":"0000-0002-5008-6530","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","full_name":"Henzinger, Monika H","first_name":"Monika H","last_name":"Henzinger"}]},{"conference":{"end_date":"2023-07-14","location":"Paderborn, Germany","name":"ICALP: Automata, Languages and Programming","start_date":"2023-07-10"},"ddc":["000"],"has_accepted_license":"1","quality_controlled":"1","file":[{"file_id":"14090","date_updated":"2023-08-21T07:04:36Z","file_size":930943,"access_level":"open_access","checksum":"a5eef225014e003efbfbe4830fdd23cb","relation":"main_file","content_type":"application/pdf","file_name":"2023_LIPIcsICALP_HenzingerM.pdf","date_created":"2023-08-21T07:04:36Z","success":1,"creator":"dernst"}],"corr_author":"1","publication":"50th International Colloquium on Automata, Languages, and Programming","publication_identifier":{"issn":["1868-8969"],"isbn":["9783959772785"]},"doi":"10.4230/LIPIcs.ICALP.2023.74","article_number":"74","year":"2023","date_created":"2023-08-20T22:01:14Z","title":"Faster submodular maximization for several classes of matroids","abstract":[{"text":"The maximization of submodular functions have found widespread application in areas such as machine learning, combinatorial optimization, and economics, where practitioners often wish to enforce various constraints; the matroid constraint has been investigated extensively due to its algorithmic properties and expressive power. Though tight approximation algorithms for general matroid constraints exist in theory, the running times of such algorithms typically scale quadratically, and are not practical for truly large scale settings. Recent progress has focused on fast algorithms for important classes of matroids given in explicit form. Currently, nearly-linear time algorithms only exist for graphic and partition matroids [Alina Ene and Huy L. Nguyen, 2019]. In this work, we develop algorithms for monotone submodular maximization constrained by graphic, transversal matroids, or laminar matroids in time near-linear in the size of their representation. Our algorithms achieve an optimal approximation of 1-1/e-ε and both generalize and accelerate the results of Ene and Nguyen [Alina Ene and Huy L. Nguyen, 2019]. In fact, the running time of our algorithm cannot be improved within the fast continuous greedy framework of Badanidiyuru and Vondrák [Ashwinkumar Badanidiyuru and Jan Vondrák, 2014].\r\nTo achieve near-linear running time, we make use of dynamic data structures that maintain bases with approximate maximum cardinality and weight under certain element updates. These data structures need to support a weight decrease operation and a novel Freeze operation that allows the algorithm to freeze elements (i.e. force to be contained) in its basis regardless of future data structure operations. For the laminar matroid, we present a new dynamic data structure using the top tree interface of Alstrup, Holm, de Lichtenberg, and Thorup [Stephen Alstrup et al., 2005] that maintains the maximum weight basis under insertions and deletions of elements in O(log n) time. This data structure needs to support certain subtree query and path update operations that are performed every insertion and deletion that are non-trivial to handle in conjunction. For the transversal matroid the Freeze operation corresponds to requiring the data structure to keep a certain set S of vertices matched, a property that we call S-stability. While there is a large body of work on dynamic matching algorithms, none are S-stable and maintain an approximate maximum weight matching under vertex updates. We give the first such algorithm for bipartite graphs with total running time linear (up to log factors) in the number of edges.","lang":"eng"}],"publication_status":"published","external_id":{"arxiv":["2305.00122"]},"article_processing_charge":"Yes","volume":261,"author":[{"id":"540c9bbd-f2de-11ec-812d-d04a5be85630","orcid":"0000-0002-5008-6530","full_name":"Henzinger, Monika H","first_name":"Monika H","last_name":"Henzinger"},{"first_name":"Paul","last_name":"Liu","full_name":"Liu, Paul"},{"first_name":"Jan","last_name":"Vondrák","full_name":"Vondrák, Jan"},{"full_name":"Zheng, Da Wei","first_name":"Da Wei","last_name":"Zheng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"call_identifier":"H2020","name":"The design and evaluation of modern fully dynamic data structures","_id":"bd9ca328-d553-11ed-ba76-dc4f890cfe62","grant_number":"101019564"},{"_id":"bda196b2-d553-11ed-ba76-8e8ee6c21103","grant_number":"I05982","name":"Static and Dynamic Hierarchical Graph Decompositions"},{"name":"Fast Algorithms for a Reactive Network Layer","grant_number":"P33775","_id":"bd9e3a2e-d553-11ed-ba76-8aa684ce17fe"}],"citation":{"short":"M. Henzinger, P. Liu, J. Vondrák, D.W. Zheng, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.","ieee":"M. Henzinger, P. Liu, J. Vondrák, and D. W. Zheng, “Faster submodular maximization for several classes of matroids,” in <i>50th International Colloquium on Automata, Languages, and Programming</i>, Paderborn, Germany, 2023, vol. 261.","ista":"Henzinger M, Liu P, Vondrák J, Zheng DW. 2023. Faster submodular maximization for several classes of matroids. 50th International Colloquium on Automata, Languages, and Programming. ICALP: Automata, Languages and Programming, LIPIcs, vol. 261, 74.","ama":"Henzinger M, Liu P, Vondrák J, Zheng DW. Faster submodular maximization for several classes of matroids. In: <i>50th International Colloquium on Automata, Languages, and Programming</i>. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">10.4230/LIPIcs.ICALP.2023.74</a>","chicago":"Henzinger, Monika, Paul Liu, Jan Vondrák, and Da Wei Zheng. “Faster Submodular Maximization for Several Classes of Matroids.” In <i>50th International Colloquium on Automata, Languages, and Programming</i>, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">https://doi.org/10.4230/LIPIcs.ICALP.2023.74</a>.","apa":"Henzinger, M., Liu, P., Vondrák, J., &#38; Zheng, D. W. (2023). Faster submodular maximization for several classes of matroids. In <i>50th International Colloquium on Automata, Languages, and Programming</i> (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">https://doi.org/10.4230/LIPIcs.ICALP.2023.74</a>","mla":"Henzinger, Monika, et al. “Faster Submodular Maximization for Several Classes of Matroids.” <i>50th International Colloquium on Automata, Languages, and Programming</i>, vol. 261, 74, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">10.4230/LIPIcs.ICALP.2023.74</a>."},"status":"public","day":"01","alternative_title":["LIPIcs"],"ec_funded":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"department":[{"_id":"MoHe"}],"arxiv":1,"publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","date_updated":"2025-07-10T11:50:45Z","month":"07","language":[{"iso":"eng"}],"date_published":"2023-07-01T00:00:00Z","oa":1,"file_date_updated":"2023-08-21T07:04:36Z","_id":"14086","scopus_import":"1","acknowledgement":" Monika Henzinger: This project has received funding from the European Research Council\r\n(ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant\r\nagreement No. 101019564 “The Design of Modern Fully Dynamic Data Structures (MoDynStruct)” and from the Austrian Science Fund (FWF) project “Static and Dynamic Hierarchical Graph Decompositions”, I 5982-N, and project “Fast Algorithms for a Reactive Network Layer (ReactNet)”, P 33775-N, with additional funding from the netidee SCIENCE Stiftung, 2020–2024. Jan Vondrák: Supported by NSF Award 2127781.","type":"conference","intvolume":"       261","oa_version":"Published Version"},{"scopus_import":"1","_id":"14105","oa_version":"Preprint","type":"conference","date_published":"2023-02-06T00:00:00Z","language":[{"iso":"eng"}],"month":"02","date_updated":"2023-09-06T10:26:56Z","publisher":"Institute of Electrical and Electronics Engineers","arxiv":1,"department":[{"_id":"FrLo"}],"oa":1,"day":"06","status":"public","citation":{"chicago":"Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele. “TeST: Test-Time Self-Training under Distribution Shift.” In <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Institute of Electrical and Electronics Engineers, 2023. <a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">https://doi.org/10.1109/wacv56688.2023.00278</a>.","apa":"Sinha, S., Gehler, P., Locatello, F., &#38; Schiele, B. (2023). TeST: Test-time Self-Training under distribution shift. In <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Waikoloa, HI, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">https://doi.org/10.1109/wacv56688.2023.00278</a>","mla":"Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.” <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>, Institute of Electrical and Electronics Engineers, 2023, doi:<a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">10.1109/wacv56688.2023.00278</a>.","ieee":"S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>, Waikoloa, HI, United States, 2023.","short":"S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023.","ama":"Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under distribution shift. In: <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Institute of Electrical and Electronics Engineers; 2023. doi:<a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">10.1109/wacv56688.2023.00278</a>","ista":"Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision."},"author":[{"full_name":"Sinha, Samarth","first_name":"Samarth","last_name":"Sinha"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"},{"last_name":"Schiele","first_name":"Bernt","full_name":"Schiele, Bernt"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","extern":"1","title":"TeST: Test-time Self-Training under distribution shift","article_processing_charge":"No","publication_status":"published","external_id":{"arxiv":["2209.11459"]},"abstract":[{"lang":"eng","text":"Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline testtime adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms [4, 43], while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks:\r\nobject detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new stateof-the art for test-time domain adaptation algorithms. "}],"doi":"10.1109/wacv56688.2023.00278","publication_identifier":{"eissn":["2642-9381"],"isbn":["9781665493475"]},"publication":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision","date_created":"2023-08-21T12:11:38Z","year":"2023","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2209.11459"}],"conference":{"name":"WACV: Winter Conference on Applications of Computer Vision","start_date":"2023-01-02","end_date":"2023-01-07","location":"Waikoloa, HI, United States"},"quality_controlled":"1"},{"_id":"14192","scopus_import":"1","type":"journal_article","acknowledgement":"D.M. and K.M. thank Robert Seiringer for helpful discussions. Open access funding provided by Institute of Science and Technology (IST Austria). Financial support from the Agence Nationale de la Recherche (ANR) through the projects ANR-17-CE40-0016, ANR-17-CE40-0007-01, ANR-17-EURE-0002 (J.L.) and from the European Union’s Horizon 2020 research and innovation programme under the Maria Skłodowska-Curie grant agreement No. 665386 (K.M.) is gratefully acknowledged.","article_type":"original","intvolume":"        26","oa_version":"Published Version","department":[{"_id":"RoSe"}],"publisher":"Springer Nature","arxiv":1,"date_updated":"2024-10-09T21:06:41Z","language":[{"iso":"eng"}],"month":"07","date_published":"2023-07-26T00:00:00Z","oa":1,"file_date_updated":"2023-08-23T10:59:15Z","isi":1,"day":"26","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"citation":{"ista":"Lampart J, Mitrouskas DJ, Mysliwy K. 2023. On the global minimum of the energy–momentum relation for the polaron. Mathematical Physics, Analysis and Geometry. 26(3), 17.","ama":"Lampart J, Mitrouskas DJ, Mysliwy K. On the global minimum of the energy–momentum relation for the polaron. <i>Mathematical Physics, Analysis and Geometry</i>. 2023;26(3). doi:<a href=\"https://doi.org/10.1007/s11040-023-09460-x\">10.1007/s11040-023-09460-x</a>","ieee":"J. Lampart, D. J. Mitrouskas, and K. Mysliwy, “On the global minimum of the energy–momentum relation for the polaron,” <i>Mathematical Physics, Analysis and Geometry</i>, vol. 26, no. 3. Springer Nature, 2023.","short":"J. Lampart, D.J. Mitrouskas, K. Mysliwy, Mathematical Physics, Analysis and Geometry 26 (2023).","mla":"Lampart, Jonas, et al. “On the Global Minimum of the Energy–Momentum Relation for the Polaron.” <i>Mathematical Physics, Analysis and Geometry</i>, vol. 26, no. 3, 17, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1007/s11040-023-09460-x\">10.1007/s11040-023-09460-x</a>.","chicago":"Lampart, Jonas, David Johannes Mitrouskas, and Krzysztof Mysliwy. “On the Global Minimum of the Energy–Momentum Relation for the Polaron.” <i>Mathematical Physics, Analysis and Geometry</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1007/s11040-023-09460-x\">https://doi.org/10.1007/s11040-023-09460-x</a>.","apa":"Lampart, J., Mitrouskas, D. J., &#38; Mysliwy, K. (2023). On the global minimum of the energy–momentum relation for the polaron. <i>Mathematical Physics, Analysis and Geometry</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s11040-023-09460-x\">https://doi.org/10.1007/s11040-023-09460-x</a>"},"volume":26,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Jonas","last_name":"Lampart","full_name":"Lampart, Jonas"},{"first_name":"David Johannes","last_name":"Mitrouskas","id":"cbddacee-2b11-11eb-a02e-a2e14d04e52d","full_name":"Mitrouskas, David Johannes"},{"id":"316457FC-F248-11E8-B48F-1D18A9856A87","full_name":"Mysliwy, Krzysztof","first_name":"Krzysztof","last_name":"Mysliwy"}],"title":"On the global minimum of the energy–momentum relation for the polaron","keyword":["Geometry and Topology","Mathematical Physics"],"abstract":[{"lang":"eng","text":"For the Fröhlich model of the large polaron, we prove that the ground state energy as a function of the total momentum has a unique global minimum at momentum zero. This implies the non-existence of a ground state of the translation invariant Fröhlich Hamiltonian and thus excludes the possibility of a localization transition at finite coupling."}],"external_id":{"isi":["001032992600001"],"arxiv":["2206.14708"]},"publication_status":"published","article_processing_charge":"Yes (via OA deal)","publication":"Mathematical Physics, Analysis and Geometry","issue":"3","publication_identifier":{"eissn":["1572-9656"],"issn":["1385-0172"]},"doi":"10.1007/s11040-023-09460-x","article_number":"17","year":"2023","date_created":"2023-08-22T14:09:47Z","ddc":["510"],"has_accepted_license":"1","quality_controlled":"1","file":[{"file_id":"14225","date_updated":"2023-08-23T10:59:15Z","file_size":317026,"checksum":"f0941cc66cb3ed06a12ca4b7e356cfd6","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_name":"2023_MathPhysics_Lampart.pdf","date_created":"2023-08-23T10:59:15Z","creator":"dernst","success":1}],"corr_author":"1"},{"_id":"14208","oa_version":"Preprint","intvolume":"       202","type":"conference","date_published":"2023-05-30T00:00:00Z","month":"05","language":[{"iso":"eng"}],"date_updated":"2023-09-13T08:46:46Z","department":[{"_id":"FrLo"}],"publisher":"ML Research Press","arxiv":1,"oa":1,"alternative_title":["PMLR"],"day":"30","status":"public","citation":{"ama":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: <i>Proceedings of the 40th International Conference on Machine Learning</i>. Vol 202. ML Research Press; 2023:43105-43128.","ista":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting in deep neural networks under lazy training. Proceedings of the 40th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 202, 43105–43128.","short":"Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.","ieee":"Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in <i>Proceedings of the 40th International Conference on Machine Learning</i>, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128.","mla":"Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy Training.” <i>Proceedings of the 40th International Conference on Machine Learning</i>, vol. 202, ML Research Press, 2023, pp. 43105–28.","apa":"Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., &#38; Cevher, V. (2023). Benign overfitting in deep neural networks under lazy training. In <i>Proceedings of the 40th International Conference on Machine Learning</i> (Vol. 202, pp. 43105–43128). Honolulu, Hawaii, United States: ML Research Press.","chicago":"Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.” In <i>Proceedings of the 40th International Conference on Machine Learning</i>, 202:43105–28. ML Research Press, 2023."},"author":[{"first_name":"Zhenyu","last_name":"Zhu","full_name":"Zhu, Zhenyu"},{"first_name":"Fanghui","last_name":"Liu","full_name":"Liu, Fanghui"},{"first_name":"Grigorios G","last_name":"Chrysos","full_name":"Chrysos, Grigorios G"},{"first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"},{"full_name":"Cevher, Volkan","first_name":"Volkan","last_name":"Cevher"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","extern":"1","volume":202,"title":"Benign overfitting in deep neural networks under lazy training","article_processing_charge":"No","page":"43105-43128","publication_status":"published","external_id":{"arxiv":["2305.19377"]},"abstract":[{"text":"This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly) zero-training error under the lazy training regime. For this purpose, we unify three interrelated concepts of overparameterization, benign overfitting, and the Lipschitz constant of DNNs. Our results indicate that interpolating with smoother functions leads to better generalization. Furthermore, we investigate the special case where interpolating smooth ground-truth functions is performed by DNNs under the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates that the generalization error converges to a constant order that only depends on label noise and initialization noise, which theoretically verifies benign overfitting. Our analysis provides a tight lower bound on the normalized margin under non-smooth activation functions, as well as the minimum eigenvalue of NTK under high-dimensional settings, which has its own interest in learning theory.","lang":"eng"}],"publication":"Proceedings of the 40th International Conference on Machine Learning","date_created":"2023-08-22T14:18:18Z","year":"2023","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2305.19377","open_access":"1"}],"conference":{"start_date":"2023-07-23","name":"International Conference on Machine Learning","location":"Honolulu, Hawaii, United States","end_date":"2023-07-29"},"quality_controlled":"1"},{"_id":"14209","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Burg","first_name":"Max F.","full_name":"Burg, Max F."},{"full_name":"Wenzel, Florian","first_name":"Florian","last_name":"Wenzel"},{"first_name":"Dominik","last_name":"Zietlow","full_name":"Zietlow, Dominik"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"full_name":"Makansi, Osama","last_name":"Makansi","first_name":"Osama"},{"first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"}],"oa_version":"Preprint","extern":"1","type":"preprint","date_published":"2023-04-20T00:00:00Z","language":[{"iso":"eng"}],"month":"04","title":"A data augmentation perspective on diffusion models and retrieval","date_updated":"2023-09-13T08:51:56Z","department":[{"_id":"FrLo"}],"arxiv":1,"article_processing_charge":"No","publication_status":"submitted","external_id":{"arxiv":["2304.10253"]},"oa":1,"abstract":[{"text":"Diffusion models excel at generating photorealistic images from text-queries. Naturally, many approaches have been proposed to use these generative abilities to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large noisily supervised, but nonetheless, annotated datasets. It is an open question whether the generalization capabilities of diffusion models beyond using the additional data of the pre-training process for augmentation lead to improved downstream performance. We perform a systematic evaluation of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. While we find that personalizing diffusion models towards the target data outperforms simpler prompting strategies, we also show that using the training data of the diffusion model alone, via a simple nearest neighbor retrieval procedure, leads to even stronger downstream performance. Overall, our study probes the limitations of diffusion models for data augmentation but also highlights its potential in generating new training data to improve performance on simple downstream vision tasks.","lang":"eng"}],"doi":"10.48550/arXiv.2304.10253","day":"20","status":"public","publication":"arXiv","date_created":"2023-08-22T14:18:43Z","year":"2023","article_number":"2304.10253","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2304.10253","open_access":"1"}],"citation":{"ieee":"M. F. Burg <i>et al.</i>, “A data augmentation perspective on diffusion models and retrieval,” <i>arXiv</i>. .","short":"M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.).","ista":"Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253.","ama":"Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>","apa":"Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &#38; Russell, C. (n.d.). A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>","chicago":"Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>.","mla":"Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, 2304.10253, doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>."}},{"conference":{"end_date":"2023-04-14","location":"Tübingen, Germany","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2304.03265"}],"quality_controlled":"1","citation":{"ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery with score matching on additive models with arbitrary noise. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","mla":"Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Causal discovery with score matching on additive models with arbitrary noise. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany."},"publication":"2nd Conference on Causal Learning and Reasoning","day":"01","status":"public","date_created":"2023-08-22T14:19:21Z","year":"2023","date_updated":"2024-10-14T12:30:04Z","arxiv":1,"department":[{"_id":"FrLo"}],"date_published":"2023-04-01T00:00:00Z","language":[{"iso":"eng"}],"month":"04","title":"Causal discovery with score matching on additive models with arbitrary noise","publication_status":"published","external_id":{"arxiv":["2304.03265"]},"abstract":[{"lang":"eng","text":"Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian noise assumption on additive non-linear models, which is common to many causal discovery approaches. In this paper we show the shortcomings of inference under this hypothesis, analyzing the risk of edge inversion under violation of Gaussianity of the noise terms. Then, we propose a novel method for inferring the topological ordering of the variables in the causal graph, from data generated according to an additive non-linear model with a generic noise distribution. This leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm with a minimal set of assumptions and state of the art performance, experimentally benchmarked on synthetic data."}],"oa":1,"article_processing_charge":"No","scopus_import":"1","_id":"14211","type":"conference","author":[{"first_name":"Francesco","last_name":"Montagna","full_name":"Montagna, Francesco"},{"first_name":"Nicoletta","last_name":"Noceti","full_name":"Noceti, Nicoletta"},{"last_name":"Rosasco","first_name":"Lorenzo","full_name":"Rosasco, Lorenzo"},{"full_name":"Zhang, Kun","last_name":"Zhang","first_name":"Kun"},{"last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","extern":"1"},{"type":"conference","extern":"1","author":[{"full_name":"Montagna, Francesco","last_name":"Montagna","first_name":"Francesco"},{"full_name":"Noceti, Nicoletta","last_name":"Noceti","first_name":"Nicoletta"},{"first_name":"Lorenzo","last_name":"Rosasco","full_name":"Rosasco, Lorenzo"},{"full_name":"Zhang, Kun","last_name":"Zhang","first_name":"Kun"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","_id":"14212","scopus_import":"1","oa":1,"abstract":[{"lang":"eng","text":"This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function ∇logp(X), we extend the work of Rolland et al. (2022) that only recovers the topological order from the score and requires an expensive pruning step removing spurious edges among those admitted by the ordering. Our analysis leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces the complexity of the pruning by a factor proportional to the graph size. In practice, DAS achieves competitive accuracy with current state-of-the-art while being over an order of magnitude faster. Overall, our approach enables principled and scalable causal discovery, significantly lowering the compute bar."}],"publication_status":"published","external_id":{"arxiv":["2304.03382"]},"article_processing_charge":"No","arxiv":1,"department":[{"_id":"FrLo"}],"date_updated":"2024-10-14T12:30:15Z","month":"04","language":[{"iso":"eng"}],"title":"Scalable causal discovery with score matching","date_published":"2023-04-01T00:00:00Z","year":"2023","date_created":"2023-08-22T14:19:40Z","status":"public","day":"01","publication":"2nd Conference on Causal Learning and Reasoning","quality_controlled":"1","citation":{"apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Scalable causal discovery with score matching. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","mla":"Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal discovery with score matching. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning."},"conference":{"location":"Tübingen, Germany","end_date":"2023-04-14","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2304.03382"}]},{"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2301.05169","open_access":"1"}],"conference":{"location":"Tübingen, Germany","end_date":"2023-04-14","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"citation":{"short":"Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ieee":"Y. Liu <i>et al.</i>, “Causal triplet: An open challenge for intervention-centric causal representation learning,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","ama":"Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ista":"Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023. Causal triplet: An open challenge for intervention-centric causal representation learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","apa":"Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &#38; Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric causal representation learning. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","chicago":"Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","mla":"Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023."},"quality_controlled":"1","day":"12","publication":"2nd Conference on Causal Learning and Reasoning","status":"public","year":"2023","date_created":"2023-08-22T14:20:18Z","month":"04","language":[{"iso":"eng"}],"title":"Causal triplet: An open challenge for intervention-centric causal representation learning","date_published":"2023-04-12T00:00:00Z","department":[{"_id":"FrLo"}],"arxiv":1,"date_updated":"2024-10-14T12:30:42Z","article_processing_charge":"No","oa":1,"abstract":[{"lang":"eng","text":"Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work."}],"publication_status":"published","external_id":{"arxiv":["2301.05169"]},"_id":"14214","extern":"1","author":[{"full_name":"Liu, Yuejiang","first_name":"Yuejiang","last_name":"Liu"},{"first_name":"Alexandre","last_name":"Alahi","full_name":"Alahi, Alexandre"},{"last_name":"Russell","first_name":"Chris","full_name":"Russell, Chris"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"last_name":"Zietlow","first_name":"Dominik","full_name":"Zietlow, Dominik"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"}],"oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference"},{"citation":{"short":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello, in:, 37th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2023, pp. 15303–15319.","ieee":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello, “ASIF: Coupled data turns unimodal models to multimodal without training,” in <i>37th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2023, vol. 36, pp. 15303–15319.","ista":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. 2023. ASIF: Coupled data turns unimodal models to multimodal without training. 37th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 36, 15303–15319.","ama":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. In: <i>37th Conference on Neural Information Processing Systems</i>. Vol 36. Neural Information Processing Systems Foundation; 2023:15303-15319.","chicago":"Norelli, Antonio, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” In <i>37th Conference on Neural Information Processing Systems</i>, 36:15303–19. Neural Information Processing Systems Foundation, 2023.","apa":"Norelli, A., Fumero, M., Maiorca, V., Moschella, L., Rodolà, E., &#38; Locatello, F. (2023). ASIF: Coupled data turns unimodal models to multimodal without training. In <i>37th Conference on Neural Information Processing Systems</i> (Vol. 36, pp. 15303–15319). New Orleans, LA, United States: Neural Information Processing Systems Foundation.","mla":"Norelli, Antonio, et al. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” <i>37th Conference on Neural Information Processing Systems</i>, vol. 36, Neural Information Processing Systems Foundation, 2023, pp. 15303–19."},"alternative_title":["Advances in Neural Information Processing Systems"],"status":"public","day":"04","OA_type":"green","month":"10","language":[{"iso":"eng"}],"date_published":"2023-10-04T00:00:00Z","arxiv":1,"publisher":"Neural Information Processing Systems Foundation","department":[{"_id":"FrLo"}],"date_updated":"2025-05-14T11:28:52Z","oa":1,"file_date_updated":"2025-02-04T12:16:13Z","_id":"14216","oa_version":"Preprint","intvolume":"        36","acknowledgement":"AN, MF, and FL partially worked on ASIF when they were at Amazon Web Services in Tübingen,\r\nGermany. This paper is financially supported by the PRIN 2020 project no.2020TA3K9N (LEGO.AI), PNRR MUR project PE0000013-FAIR, and ERC Grant no.802554 (SPECGEO).","type":"conference","has_accepted_license":"1","ddc":["000"],"conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2023-12-12","location":"New Orleans, LA, United States","end_date":"2023-12-14"},"corr_author":"1","file":[{"file_id":"18994","date_updated":"2025-02-04T12:16:13Z","file_size":12648978,"access_level":"open_access","checksum":"e51c90300b92d7135050da5c9e3a8015","relation":"main_file","file_name":"2023_NeurIPS_Fumero.pdf","content_type":"application/pdf","date_created":"2025-02-04T12:16:13Z","success":1,"creator":"dernst"}],"quality_controlled":"1","publication_identifier":{"isbn":["9781713899921"]},"publication":"37th Conference on Neural Information Processing Systems","year":"2023","date_created":"2023-08-22T14:22:04Z","title":"ASIF: Coupled data turns unimodal models to multimodal without training","page":"15303-15319","article_processing_charge":"No","abstract":[{"text":"CLIP proved that aligning visual and language spaces is key to solving many vision tasks without explicit training, but required to train image and text encoders from scratch on a huge dataset. LiT improved this by only training the text encoder and using a pre-trained vision network. In this paper, we show that a common space can be created without any training at all, using single-domain encoders (trained with or without supervision) and a much smaller amount of image-text pairs. Furthermore, our model has unique properties. Most notably, deploying a new version with updated training samples can be done in a matter of seconds. Additionally, the representations in the common space are easily interpretable as every dimension corresponds to the similarity of the input to a unique entry in the multimodal dataset. Experiments on standard zero-shot visual benchmarks demonstrate the typical transfer ability of image-text models. Overall, our method represents a simple yet surprisingly strong baseline for foundation multi-modal models, raising important questions on their data efficiency and on the role of retrieval in machine learning.","lang":"eng"}],"publication_status":"published","external_id":{"arxiv":["2210.01738"]},"related_material":{"link":[{"relation":"software","url":"https://github.com/noranta4/ASIF"}]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Norelli, Antonio","last_name":"Norelli","first_name":"Antonio"},{"last_name":"Fumero","first_name":"Marco","full_name":"Fumero, Marco"},{"first_name":"Valentino","last_name":"Maiorca","full_name":"Maiorca, Valentino"},{"full_name":"Moschella, Luca","first_name":"Luca","last_name":"Moschella"},{"last_name":"Rodolà","first_name":"Emanuele","full_name":"Rodolà, Emanuele"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"}],"volume":36},{"article_processing_charge":"No","oa":1,"abstract":[{"lang":"eng","text":"Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the random weights initialization, training hyperparameters, or other sources of randomness in the training phase may induce incoherent latent spaces that hinder any form of reuse. Nevertheless, we empirically observe that, under the same data and modeling choices, the angles between the encodings within distinct latent spaces do not change. In this work, we propose the latent similarity between each sample and a fixed set of anchors as an alternative data representation, demonstrating that it can enforce the desired invariances without any additional training. We show how neural architectures can leverage these relative representations to guarantee, in practice, invariance to latent isometries and rescalings, effectively enabling latent space communication: from zero-shot model stitching to latent space comparison between diverse settings. We extensively validate the generalization capability of our approach on different datasets, spanning various modalities (images, text, graphs), tasks (e.g., classification, reconstruction) and architectures (e.g., CNNs, GCNs, transformers)."}],"publication_status":"published","external_id":{"arxiv":["2209.15430"]},"title":"Relative representations enable zero-shot latent space communication","language":[{"iso":"eng"}],"month":"05","date_published":"2023-05-01T00:00:00Z","department":[{"_id":"FrLo"}],"arxiv":1,"date_updated":"2023-09-13T09:44:26Z","extern":"1","author":[{"last_name":"Moschella","first_name":"Luca","full_name":"Moschella, Luca"},{"full_name":"Maiorca, Valentino","first_name":"Valentino","last_name":"Maiorca"},{"last_name":"Fumero","first_name":"Marco","full_name":"Fumero, Marco"},{"first_name":"Antonio","last_name":"Norelli","full_name":"Norelli, Antonio"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"},{"first_name":"Emanuele","last_name":"Rodolà","full_name":"Rodolà, Emanuele"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","type":"conference","_id":"14217","citation":{"short":"L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà, in:, The 11th International Conference on Learning Representations, 2023.","ieee":"L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà, “Relative representations enable zero-shot latent space communication,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ama":"Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative representations enable zero-shot latent space communication. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","ista":"Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. 2023. Relative representations enable zero-shot latent space communication. The 11th International Conference on Learning Representations. International Conference on Machine Learning Representations.","apa":"Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., &#38; Rodolà, E. (2023). Relative representations enable zero-shot latent space communication. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","chicago":"Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent Space Communication.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","mla":"Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space Communication.” <i>The 11th International Conference on Learning Representations</i>, 2023."},"quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/2209.15430","open_access":"1"}],"conference":{"start_date":"2023-05-01","name":"International Conference on Machine Learning Representations","end_date":"2023-05-05","location":"Kigali, Rwanda"},"year":"2023","date_created":"2023-08-22T14:22:20Z","day":"01","publication":"The 11th International Conference on Learning Representations","status":"public"},{"article_processing_charge":"No","publication_status":"published","external_id":{"arxiv":["2209.14860"]},"abstract":[{"lang":"eng","text":"Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing image-based object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature."}],"oa":1,"date_published":"2023-05-10T00:00:00Z","title":"Bridging the gap to real-world object-centric learning","month":"05","language":[{"iso":"eng"}],"date_updated":"2024-10-14T12:30:54Z","department":[{"_id":"FrLo"}],"arxiv":1,"author":[{"full_name":"Seitzer, Maximilian","first_name":"Maximilian","last_name":"Seitzer"},{"full_name":"Horn, Max","last_name":"Horn","first_name":"Max"},{"first_name":"Andrii","last_name":"Zadaianchuk","full_name":"Zadaianchuk, Andrii"},{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"full_name":"Xiao, Tianjun","last_name":"Xiao","first_name":"Tianjun"},{"first_name":"Carl-Johann Simon-Gabriel","last_name":"Carl-Johann Simon-Gabriel","full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel"},{"full_name":"He, Tong","last_name":"He","first_name":"Tong"},{"full_name":"Zhang, Zheng","last_name":"Zhang","first_name":"Zheng"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"},{"first_name":"Thomas","last_name":"Brox","full_name":"Brox, Thomas"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","first_name":"Francesco","last_name":"Locatello"}],"oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","extern":"1","type":"conference","_id":"14218","citation":{"short":"M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The 11th International Conference on Learning Representations, 2023.","ieee":"M. Seitzer <i>et al.</i>, “Bridging the gap to real-world object-centric learning,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ista":"Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap to real-world object-centric learning. The 11th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","ama":"Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric learning. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","apa":"Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world object-centric learning. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","chicago":"Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging the Gap to Real-World Object-Centric Learning.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","mla":"Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric Learning.” <i>The 11th International Conference on Learning Representations</i>, 2023."},"quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/2209.14860","open_access":"1"}],"conference":{"location":"Kigali, Rwanda","end_date":"2023-05-05","name":"ICLR: International Conference on Learning Representations","start_date":"2023-05-01"},"date_created":"2023-08-22T14:22:41Z","year":"2023","status":"public","publication":"The 11th International Conference on Learning Representations","day":"10"},{"citation":{"chicago":"Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","apa":"Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., &#38; Brox, T. (2023). Unsupervised semantic segmentation with self-supervised object-centric representations. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","mla":"Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” <i>The 11th International Conference on Learning Representations</i>, 2023.","short":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The 11th International Conference on Learning Representations, 2023.","ieee":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised semantic segmentation with self-supervised object-centric representations,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ama":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","ista":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised semantic segmentation with self-supervised object-centric representations. The 11th International Conference on Learning Representations. ICLR: International Conference on Learning Representations."},"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2207.05027"}],"conference":{"location":"Kigali, Rwanda","end_date":"2023-05-05","name":"ICLR: International Conference on Learning Representations","start_date":"2023-05-01"},"date_created":"2023-08-22T14:22:58Z","year":"2023","day":"01","publication":"The 11th International Conference on Learning Representations","status":"public","article_processing_charge":"No","publication_status":"published","external_id":{"arxiv":["2207.05027"]},"oa":1,"abstract":[{"lang":"eng","text":"In this paper, we show that recent advances in self-supervised feature\r\nlearning enable unsupervised object discovery and semantic segmentation with a\r\nperformance that matches the state of the field on supervised semantic\r\nsegmentation 10 years ago. We propose a methodology based on unsupervised\r\nsaliency masks and self-supervised feature clustering to kickstart object\r\ndiscovery followed by training a semantic segmentation network on pseudo-labels\r\nto bootstrap the system on images with multiple objects. We present results on\r\nPASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers 34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of 19.6 for all 81 categories."}],"date_published":"2023-05-01T00:00:00Z","language":[{"iso":"eng"}],"month":"05","title":"Unsupervised semantic segmentation with self-supervised object-centric representations","date_updated":"2023-09-13T11:25:43Z","arxiv":1,"department":[{"_id":"FrLo"}],"oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Zadaianchuk, Andrii","first_name":"Andrii","last_name":"Zadaianchuk"},{"first_name":"Matthaeus","last_name":"Kleindessner","full_name":"Kleindessner, Matthaeus"},{"full_name":"Zhu, Yi","last_name":"Zhu","first_name":"Yi"},{"first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"first_name":"Thomas","last_name":"Brox","full_name":"Brox, Thomas"}],"extern":"1","type":"conference","_id":"14219"},{"oa":1,"abstract":[{"text":"Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks of moving objects via unsupervised motion segmentation. Second, generative models are trained on the masks of the background and the moving objects, respectively. Third, background and foreground models are combined in a conditional \"dead leaves\" scene model to sample novel scene configurations where occlusions and depth layering arise naturally. To evaluate the individual stages, we introduce the Fishbowl dataset positioned between complex real-world scenes and common object-centric benchmarks of simplistic objects. We show that our approach allows learning generative models that generalize beyond the occlusions present in the input videos, and represent scenes in a modular fashion that allows sampling plausible scenes outside the training distribution by permitting, for instance, object numbers or densities not observed in the training set.","lang":"eng"}],"external_id":{"arxiv":["2110.06562"]},"publication_status":"published","article_processing_charge":"No","department":[{"_id":"FrLo"}],"arxiv":1,"date_updated":"2023-09-13T11:31:14Z","title":"Unsupervised object learning via common fate","language":[{"iso":"eng"}],"month":"04","date_published":"2023-04-15T00:00:00Z","type":"conference","extern":"1","oa_version":"Preprint","author":[{"last_name":"Tangemann","first_name":"Matthias","full_name":"Tangemann, Matthias"},{"full_name":"Schneider, Steffen","first_name":"Steffen","last_name":"Schneider"},{"full_name":"Kügelgen, Julius von","first_name":"Julius von","last_name":"Kügelgen"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"},{"last_name":"Brox","first_name":"Thomas","full_name":"Brox, Thomas"},{"full_name":"Kümmerer, Matthias","last_name":"Kümmerer","first_name":"Matthias"},{"first_name":"Matthias","last_name":"Bethge","full_name":"Bethge, Matthias"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"14222","quality_controlled":"1","citation":{"short":"M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T. Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ieee":"M. Tangemann <i>et al.</i>, “Unsupervised object learning via common fate,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","ama":"Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ista":"Tangemann M, Schneider S, Kügelgen J von, Locatello F, Gehler P, Brox T, Kümmerer M, Bethge M, Schölkopf B. 2023. Unsupervised object learning via common fate. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning, 2110.06562.","chicago":"Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P., Brox, T., … Schölkopf, B. (2023). Unsupervised object learning via common fate. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","mla":"Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2110.06562, 2023."},"conference":{"location":"Tübingen, Germany","end_date":"2023-04-14","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2110.06562"}],"article_number":"2110.06562","year":"2023","date_created":"2023-08-22T14:23:54Z","day":"15","status":"public","publication":"2nd Conference on Causal Learning and Reasoning"},{"doi":"10.1103/PhysRevLett.131.053201","publication_identifier":{"eissn":["1079-7114"],"issn":["0031-9007"]},"issue":"5","publication":"Physical Review Letters","date_created":"2023-08-27T22:01:16Z","year":"2023","article_number":"053201","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2308.15247"}],"quality_controlled":"1","pmid":1,"author":[{"last_name":"Kranabetter","first_name":"Lorenz","full_name":"Kranabetter, Lorenz"},{"first_name":"Henrik H.","last_name":"Kristensen","full_name":"Kristensen, Henrik H."},{"orcid":"0000-0001-9666-3543","id":"4AF46FD6-F248-11E8-B48F-1D18A9856A87","full_name":"Ghazaryan, Areg","first_name":"Areg","last_name":"Ghazaryan"},{"full_name":"Schouder, Constant A.","first_name":"Constant A.","last_name":"Schouder"},{"full_name":"Chatterley, Adam S.","first_name":"Adam S.","last_name":"Chatterley"},{"first_name":"Paul","last_name":"Janssen","full_name":"Janssen, Paul"},{"full_name":"Jensen, Frank","last_name":"Jensen","first_name":"Frank"},{"first_name":"Robert E.","last_name":"Zillich","full_name":"Zillich, Robert E."},{"last_name":"Lemeshko","first_name":"Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail"},{"full_name":"Stapelfeldt, Henrik","first_name":"Henrik","last_name":"Stapelfeldt"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":131,"title":"Nonadiabatic laser-induced alignment dynamics of molecules on a surface","article_processing_charge":"No","external_id":{"isi":["001101784100001"],"pmid":["37595218"],"arxiv":["2308.15247"]},"publication_status":"published","abstract":[{"text":"We demonstrate that a sodium dimer, Na2(13Σ+u), residing on the surface of a helium nanodroplet, can be set into rotation by a nonresonant 1.0 ps infrared laser pulse. The time-dependent degree of alignment measured, exhibits a periodic, gradually decreasing structure that deviates qualitatively from that expected for gas-phase dimers. Comparison to alignment dynamics calculated from the time-dependent rotational Schrödinger equation shows that the deviation is due to the alignment dependent interaction between the dimer and the droplet surface. This interaction confines the dimer to the tangential plane of the droplet surface at the point where it resides and is the reason that the observed alignment dynamics is also well described by a 2D quantum rotor model.","lang":"eng"}],"status":"public","day":"04","ec_funded":1,"citation":{"chicago":"Kranabetter, Lorenz, Henrik H. Kristensen, Areg Ghazaryan, Constant A. Schouder, Adam S. Chatterley, Paul Janssen, Frank Jensen, Robert E. Zillich, Mikhail Lemeshko, and Henrik Stapelfeldt. “Nonadiabatic Laser-Induced Alignment Dynamics of Molecules on a Surface.” <i>Physical Review Letters</i>. American Physical Society, 2023. <a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">https://doi.org/10.1103/PhysRevLett.131.053201</a>.","apa":"Kranabetter, L., Kristensen, H. H., Ghazaryan, A., Schouder, C. A., Chatterley, A. S., Janssen, P., … Stapelfeldt, H. (2023). Nonadiabatic laser-induced alignment dynamics of molecules on a surface. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">https://doi.org/10.1103/PhysRevLett.131.053201</a>","mla":"Kranabetter, Lorenz, et al. “Nonadiabatic Laser-Induced Alignment Dynamics of Molecules on a Surface.” <i>Physical Review Letters</i>, vol. 131, no. 5, 053201, American Physical Society, 2023, doi:<a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">10.1103/PhysRevLett.131.053201</a>.","ieee":"L. Kranabetter <i>et al.</i>, “Nonadiabatic laser-induced alignment dynamics of molecules on a surface,” <i>Physical Review Letters</i>, vol. 131, no. 5. American Physical Society, 2023.","short":"L. Kranabetter, H.H. Kristensen, A. Ghazaryan, C.A. Schouder, A.S. Chatterley, P. Janssen, F. Jensen, R.E. Zillich, M. Lemeshko, H. Stapelfeldt, Physical Review Letters 131 (2023).","ista":"Kranabetter L, Kristensen HH, Ghazaryan A, Schouder CA, Chatterley AS, Janssen P, Jensen F, Zillich RE, Lemeshko M, Stapelfeldt H. 2023. Nonadiabatic laser-induced alignment dynamics of molecules on a surface. Physical Review Letters. 131(5), 053201.","ama":"Kranabetter L, Kristensen HH, Ghazaryan A, et al. Nonadiabatic laser-induced alignment dynamics of molecules on a surface. <i>Physical Review Letters</i>. 2023;131(5). doi:<a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">10.1103/PhysRevLett.131.053201</a>"},"project":[{"call_identifier":"H2020","name":"Angulon: physics and applications of a new quasiparticle","_id":"2688CF98-B435-11E9-9278-68D0E5697425","grant_number":"801770"}],"scopus_import":"1","_id":"14238","intvolume":"       131","oa_version":"Preprint","article_type":"original","acknowledgement":"H. S. acknowledges support from The Villum Foundation through a Villum Investigator Grant No. 25886. M. L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). F. J. and R. E. Z. acknowledge support from the Centre for Scientific Computing, Aarhus and the JKU scientific computing administration, Linz, respectively.","type":"journal_article","date_published":"2023-08-04T00:00:00Z","month":"08","language":[{"iso":"eng"}],"date_updated":"2025-04-14T07:48:54Z","publisher":"American Physical Society","arxiv":1,"department":[{"_id":"MiLe"}],"isi":1,"oa":1},{"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ec_funded":1,"status":"public","day":"03","citation":{"chicago":"Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization Conjecture for Rational Singularities.” <i>Forum of Mathematics, Sigma</i>. Cambridge University Press, 2023. <a href=\"https://doi.org/10.1017/fms.2023.65\">https://doi.org/10.1017/fms.2023.65</a>.","apa":"Mauri, M., &#38; Shinder, E. (2023). Homological Bondal-Orlov localization conjecture for rational singularities. <i>Forum of Mathematics, Sigma</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/fms.2023.65\">https://doi.org/10.1017/fms.2023.65</a>","mla":"Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization Conjecture for Rational Singularities.” <i>Forum of Mathematics, Sigma</i>, vol. 11, e66, Cambridge University Press, 2023, doi:<a href=\"https://doi.org/10.1017/fms.2023.65\">10.1017/fms.2023.65</a>.","ieee":"M. Mauri and E. Shinder, “Homological Bondal-Orlov localization conjecture for rational singularities,” <i>Forum of Mathematics, Sigma</i>, vol. 11. Cambridge University Press, 2023.","short":"M. Mauri, E. Shinder, Forum of Mathematics, Sigma 11 (2023).","ama":"Mauri M, Shinder E. Homological Bondal-Orlov localization conjecture for rational singularities. <i>Forum of Mathematics, Sigma</i>. 2023;11. doi:<a href=\"https://doi.org/10.1017/fms.2023.65\">10.1017/fms.2023.65</a>","ista":"Mauri M, Shinder E. 2023. Homological Bondal-Orlov localization conjecture for rational singularities. Forum of Mathematics, Sigma. 11, e66."},"project":[{"call_identifier":"H2020","name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","grant_number":"101034413"}],"intvolume":"        11","oa_version":"Published Version","article_type":"original","acknowledgement":"We thank Agnieszka Bodzenta-Skibińska, Paolo Cascini, Wahei Hara, Sándor Kovács, Alexander Kuznetsov, Mircea Musta  ă, Nebojsa Pavic, Pavel Sechin, and Michael Wemyss for discussions and e-mail correspondence. We also thank the anonymous referee for the helpful comments. M.M. was supported by the Institute of Science and Technology Austria. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101034413. E.S. was partially supported by the EPSRC grant EP/T019379/1 “Derived categories and algebraic K-theory of singularities”, and by the ERC Synergy grant “Modern Aspects of Geometry: Categories, Cycles and Cohomology of Hyperkähler Varieties.”\r\n\r\n","type":"journal_article","scopus_import":"1","_id":"14239","isi":1,"file_date_updated":"2023-09-05T06:43:11Z","oa":1,"date_published":"2023-08-03T00:00:00Z","language":[{"iso":"eng"}],"month":"08","date_updated":"2025-04-14T07:54:52Z","arxiv":1,"publisher":"Cambridge University Press","department":[{"_id":"TaHa"}],"date_created":"2023-08-27T22:01:16Z","year":"2023","article_number":"e66","publication_identifier":{"eissn":["2050-5094"]},"doi":"10.1017/fms.2023.65","publication":"Forum of Mathematics, Sigma","corr_author":"1","file":[{"creator":"dernst","success":1,"date_created":"2023-09-05T06:43:11Z","file_name":"2023_ForumMathematics_Mauri.pdf","content_type":"application/pdf","checksum":"c36241750cc5cb06890aec0ecdfee626","relation":"main_file","access_level":"open_access","file_size":280865,"date_updated":"2023-09-05T06:43:11Z","file_id":"14266"}],"quality_controlled":"1","has_accepted_license":"1","ddc":["510"],"author":[{"id":"2cf70c34-09c1-11ed-bd8d-c34fac206130","full_name":"Mauri, Mirko","first_name":"Mirko","last_name":"Mauri"},{"last_name":"Shinder","first_name":"Evgeny","full_name":"Shinder, Evgeny"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":11,"article_processing_charge":"Yes","publication_status":"published","external_id":{"arxiv":["2212.06786"],"isi":["001041926700001"]},"abstract":[{"text":"Given a resolution of rational singularities  π:X~→X  over a field of characteristic zero, we use a Hodge-theoretic argument to prove that the image of the functor  Rπ∗:Db(X~)→Db(X)\r\n  between bounded derived categories of coherent sheaves generates  Db(X)\r\n  as a triangulated category. This gives a weak version of the Bondal–Orlov localization conjecture [BO02], answering a question from [PS21]. The same result is established more generally for proper (not necessarily birational) morphisms  π:X~→X , with  X~\r\n  smooth, satisfying  Rπ∗(OX~)=OX .","lang":"eng"}],"title":"Homological Bondal-Orlov localization conjecture for rational singularities"},{"article_number":"83","date_created":"2023-08-27T22:01:17Z","year":"2023","publication":"ACM Transactions on Graphics","publication_identifier":{"eissn":["1557-7368"],"issn":["0730-0301"]},"doi":"10.1145/3592098","issue":"4","file":[{"file_name":"PaperVideo_final.mp4","content_type":"video/mp4","date_created":"2023-12-21T12:26:40Z","creator":"sjeschke","success":1,"file_id":"14704","date_updated":"2023-12-21T12:26:40Z","file_size":511572575,"checksum":"1d178bb2f8011d9f5aedda6427e18c7a","relation":"main_file","access_level":"open_access"},{"creator":"dernst","success":1,"content_type":"application/pdf","file_name":"2023_ACMToG_Jeschke.pdf","date_created":"2024-01-02T09:34:27Z","file_size":7469177,"date_updated":"2024-01-02T09:34:27Z","relation":"main_file","checksum":"a49b2e744d5cd1276bb8b2e0ce6dc638","access_level":"open_access","file_id":"14725"}],"quality_controlled":"1","corr_author":"1","ddc":["000"],"has_accepted_license":"1","volume":42,"author":[{"first_name":"Stefan","last_name":"Jeschke","full_name":"Jeschke, Stefan","id":"44D6411A-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0001-6646-5546","id":"3C61F1D2-F248-11E8-B48F-1D18A9856A87","full_name":"Wojtan, Christopher J","first_name":"Christopher J","last_name":"Wojtan"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledged_ssus":[{"_id":"ScienComp"}],"publication_status":"published","external_id":{"isi":["001044671300049"]},"abstract":[{"text":"This paper introduces a novel method for simulating large bodies of water as a height field. At the start of each time step, we partition the waves into a bulk flow (which approximately satisfies the assumptions of the shallow water equations) and surface waves (which approximately satisfy the assumptions of Airy wave theory). We then solve the two wave regimes separately using appropriate state-of-the-art techniques, and re-combine the resulting wave velocities at the end of each step. This strategy leads to the first heightfield wave model capable of simulating complex interactions between both deep and shallow water effects, like the waves from a boat wake sloshing up onto a beach, or a dam break producing wave interference patterns and eddies. We also analyze the numerical dispersion created by our method and derive an exact correction factor for waves at a constant water depth, giving us a numerically perfect re-creation of theoretical water wave dispersion patterns.","lang":"eng"}],"article_processing_charge":"Yes (in subscription journal)","title":"Generalizing shallow water simulations with dispersive surface waves","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"day":"01","status":"public","project":[{"name":"Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena","_id":"34bc2376-11ca-11ed-8bc3-9a3b3961a088","grant_number":"101045083"}],"citation":{"ista":"Jeschke S, Wojtan C. 2023. Generalizing shallow water simulations with dispersive surface waves. ACM Transactions on Graphics. 42(4), 83.","ama":"Jeschke S, Wojtan C. Generalizing shallow water simulations with dispersive surface waves. <i>ACM Transactions on Graphics</i>. 2023;42(4). doi:<a href=\"https://doi.org/10.1145/3592098\">10.1145/3592098</a>","ieee":"S. Jeschke and C. Wojtan, “Generalizing shallow water simulations with dispersive surface waves,” <i>ACM Transactions on Graphics</i>, vol. 42, no. 4. Association for Computing Machinery, 2023.","short":"S. Jeschke, C. Wojtan, ACM Transactions on Graphics 42 (2023).","mla":"Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations with Dispersive Surface Waves.” <i>ACM Transactions on Graphics</i>, vol. 42, no. 4, 83, Association for Computing Machinery, 2023, doi:<a href=\"https://doi.org/10.1145/3592098\">10.1145/3592098</a>.","apa":"Jeschke, S., &#38; Wojtan, C. (2023). Generalizing shallow water simulations with dispersive surface waves. <i>ACM Transactions on Graphics</i>. Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3592098\">https://doi.org/10.1145/3592098</a>","chicago":"Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations with Dispersive Surface Waves.” <i>ACM Transactions on Graphics</i>. Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3592098\">https://doi.org/10.1145/3592098</a>."},"type":"journal_article","acknowledgement":"We thank Georg Sperl for helping with early research for this paper, Mickael Ly and Yi-Lu Chen for proofreading, and members of the ISTA Visual Computing Group for general feedback. This project was funded in part by the European Research Council (ERC Consolidator Grant 101045083 CoDiNA).\r\nThe motorboat and sailboat were modeled by Sergei and the palmtrees by YadroGames. The environment map was created by Emil Persson.","intvolume":"        42","oa_version":"Published Version","article_type":"original","scopus_import":"1","_id":"14240","file_date_updated":"2024-01-02T09:34:27Z","oa":1,"isi":1,"date_updated":"2025-04-14T08:01:13Z","publisher":"Association for Computing Machinery","department":[{"_id":"ChWo"}],"date_published":"2023-08-01T00:00:00Z","language":[{"iso":"eng"}],"month":"08"},{"author":[{"full_name":"Tojo, Kenji","last_name":"Tojo","first_name":"Kenji"},{"full_name":"Shamir, Ariel","first_name":"Ariel","last_name":"Shamir"},{"full_name":"Bickel, Bernd","id":"49876194-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6511-9385","last_name":"Bickel","first_name":"Bernd"},{"full_name":"Umetani, Nobuyuki","last_name":"Umetani","first_name":"Nobuyuki"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","title":"Stealth shaper: Reflectivity optimization as surface stylization","abstract":[{"text":"We present a technique to optimize the reflectivity of a surface while preserving its overall shape. The naïve optimization of the mesh vertices using the gradients of reflectivity simulations results in undesirable distortion. In contrast, our robust formulation optimizes the surface normal as an independent variable that bridges the reflectivity term with differential rendering, and the regularization term with as-rigid-as-possible elastic energy. We further adaptively subdivide the input mesh to improve the convergence. Consequently, our method can minimize the retroreflectivity of a wide range of input shapes, resulting in sharply creased shapes ubiquitous among stealth aircraft and Sci-Fi vehicles. Furthermore, by changing the reward for the direction of the outgoing light directions, our method can be applied to other reflectivity design tasks, such as the optimization of architectural walls to concentrate light in a specific region. We have tested the proposed method using light-transport simulations and real-world 3D-printed objects.","lang":"eng"}],"publication_status":"published","external_id":{"isi":["001117690500020"],"arxiv":["2305.05944"]},"article_processing_charge":"No","publication":"SIGGRAPH 2023 Conference Proceedings","publication_identifier":{"isbn":["9798400701597"]},"doi":"10.1145/3588432.3591542","article_number":"20","year":"2023","date_created":"2023-08-27T22:01:17Z","conference":{"location":"Los Angeles, CA, United States","end_date":"2023-08-10","start_date":"2023-08-06","name":"SIGGRAPH: Computer Graphics and Interactive Techniques Conference"},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2305.05944"}],"quality_controlled":"1","corr_author":"1","_id":"14241","scopus_import":"1","acknowledgement":"The authors would like to thank Yuki Koyama and Takeo Igarashi for early discussions, and Yuta Yaguchi for support in 3D printing. This research is partially supported by the Israel Science Foundation grant number 1390/19.\r\n","type":"conference","oa_version":"Preprint","department":[{"_id":"BeBi"}],"publisher":"Association for Computing Machinery","arxiv":1,"date_updated":"2025-09-09T12:49:15Z","language":[{"iso":"eng"}],"month":"07","date_published":"2023-07-23T00:00:00Z","oa":1,"isi":1,"day":"23","status":"public","citation":{"ista":"Tojo K, Shamir A, Bickel B, Umetani N. 2023. Stealth shaper: Reflectivity optimization as surface stylization. SIGGRAPH 2023 Conference Proceedings. SIGGRAPH: Computer Graphics and Interactive Techniques Conference, 20.","ama":"Tojo K, Shamir A, Bickel B, Umetani N. Stealth shaper: Reflectivity optimization as surface stylization. In: <i>SIGGRAPH 2023 Conference Proceedings</i>. Association for Computing Machinery; 2023. doi:<a href=\"https://doi.org/10.1145/3588432.3591542\">10.1145/3588432.3591542</a>","ieee":"K. Tojo, A. Shamir, B. Bickel, and N. Umetani, “Stealth shaper: Reflectivity optimization as surface stylization,” in <i>SIGGRAPH 2023 Conference Proceedings</i>, Los Angeles, CA, United States, 2023.","short":"K. Tojo, A. Shamir, B. Bickel, N. Umetani, in:, SIGGRAPH 2023 Conference Proceedings, Association for Computing Machinery, 2023.","mla":"Tojo, Kenji, et al. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.” <i>SIGGRAPH 2023 Conference Proceedings</i>, 20, Association for Computing Machinery, 2023, doi:<a href=\"https://doi.org/10.1145/3588432.3591542\">10.1145/3588432.3591542</a>.","chicago":"Tojo, Kenji, Ariel Shamir, Bernd Bickel, and Nobuyuki Umetani. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.” In <i>SIGGRAPH 2023 Conference Proceedings</i>. Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3588432.3591542\">https://doi.org/10.1145/3588432.3591542</a>.","apa":"Tojo, K., Shamir, A., Bickel, B., &#38; Umetani, N. (2023). Stealth shaper: Reflectivity optimization as surface stylization. In <i>SIGGRAPH 2023 Conference Proceedings</i>. Los Angeles, CA, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3588432.3591542\">https://doi.org/10.1145/3588432.3591542</a>"}}]