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European Geosciences Union; 2023. doi:<a href=\"https://doi.org/10.5194/egusphere-egu23-15870\">10.5194/egusphere-egu23-15870</a>"},"department":[{"_id":"CaMu"}],"date_published":"2023-04-23T00:00:00Z","publication_status":"published","oa_version":"Published Version","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":"23","author":[{"full_name":"Abramian, Sophie","first_name":"Sophie","last_name":"Abramian"},{"last_name":"Muller","id":"f978ccb0-3f7f-11eb-b193-b0e2bd13182b","orcid":"0000-0001-5836-5350","first_name":"Caroline J","full_name":"Muller, Caroline J"},{"last_name":"Risi","first_name":"Camille","full_name":"Risi, Camille"}],"oa":1,"_id":"14866","status":"public","type":"conference_abstract","language":[{"iso":"eng"}],"doi":"10.5194/egusphere-egu23-15870","publisher":"European Geosciences Union","date_updated":"2024-02-05T08:13:12Z","year":"2023"},{"publication_status":"published","oa_version":"Published Version","page":"36-41","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"day":"01","ec_funded":1,"abstract":[{"lang":"eng","text":"<jats:p>Starting with the empty graph on $[n]$, at each round, a set of $K=K(n)$ edges is presented chosen uniformly at random from the ones that have not been presented yet. We are then asked to choose at most one of the presented edges and add it to the current graph. Our goal is to construct a Hamiltonian graph with $(1+o(1))n$ edges within as few rounds as possible. We show that in this process, one can build a Hamiltonian graph of size $(1+o(1))n$ in $(1+o(1))(1+(\\log n)/2K) n$ rounds w.h.p. The case $K=1$ implies that w.h.p. one can build a Hamiltonian graph by choosing $(1+o(1))n$ edges in an online fashion as they appear along the first $(0.5+o(1))n\\log n$ rounds of the random graph process. This answers a question of Frieze, Krivelevich and Michaeli. Observe that the number of rounds is asymptotically optimal as the first $0.5n\\log n$ edges do not span a Hamilton cycle w.h.p. The case $K=\\Theta(\\log n)$ implies that the Hamiltonicity threshold of the corresponding Achlioptas process is at most $(1+o(1))(1+(\\log n)/2K) n$. This matches the $(1-o(1))(1+(\\log n)/2K) n$ lower bound due to Krivelevich, Lubetzky and Sudakov and resolves the problem of determining the Hamiltonicity threshold of the Achlioptas process with $K=\\Theta(\\log n)$. We also show that in the above process one can construct a graph $G$ that spans a matching of size $\\lfloor V(G)/2) \\rfloor$ and $(0.5+o(1))n$ edges within $(1+o(1))(0.5+(\\log n)/2K) n$ rounds w.h.p. Our proof relies on a robust Hamiltonicity property of the strong $4$-core of the binomial random graph which we use as a black-box. This property allows it to absorb paths covering vertices outside the strong $4$-core into a cycle.</jats:p>"}],"status":"public","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","date_updated":"2024-01-24T09:38:44Z","publication":"Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications","external_id":{"arxiv":["2209.09860"]},"title":"Constructing Hamilton cycles and perfect matchings efficiently","file":[{"creator":"dernst","access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_name":"2023_Eurocomb_Anastos.pdf","file_size":464230,"file_id":"14881","checksum":"fb1d9a1e7389d90ec0e5e76934373cf8","date_created":"2024-01-24T09:34:43Z","date_updated":"2024-01-24T09:34:43Z","success":1}],"has_accepted_license":"1","project":[{"grant_number":"101034413","call_identifier":"H2020","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program"}],"arxiv":1,"citation":{"apa":"Anastos, M. (2023). Constructing Hamilton cycles and perfect matchings efficiently. In <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i> (pp. 36–41). Prague, Czech Republic: Masaryk University Press. <a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">https://doi.org/10.5817/cz.muni.eurocomb23-005</a>","ista":"Anastos M. 2023. Constructing Hamilton cycles and perfect matchings efficiently. Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications. EUROCOMB: European Conference on Combinatorics, Graph Theory and Applications, 36–41.","short":"M. Anastos, in:, Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications, Masaryk University Press, 2023, pp. 36–41.","mla":"Anastos, Michael. “Constructing Hamilton Cycles and Perfect Matchings Efficiently.” <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>, Masaryk University Press, 2023, pp. 36–41, doi:<a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">10.5817/cz.muni.eurocomb23-005</a>.","ama":"Anastos M. Constructing Hamilton cycles and perfect matchings efficiently. In: <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>. Masaryk University Press; 2023:36-41. doi:<a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">10.5817/cz.muni.eurocomb23-005</a>","chicago":"Anastos, Michael. “Constructing Hamilton Cycles and Perfect Matchings Efficiently.” In <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>, 36–41. Masaryk University Press, 2023. <a href=\"https://doi.org/10.5817/cz.muni.eurocomb23-005\">https://doi.org/10.5817/cz.muni.eurocomb23-005</a>.","ieee":"M. Anastos, “Constructing Hamilton cycles and perfect matchings efficiently,” in <i>Proceedings of the 12th European Conference on Combinatorics, Graph Theory and Applications</i>, Prague, Czech Republic, 2023, pp. 36–41."},"date_published":"2023-09-01T00:00:00Z","author":[{"full_name":"Anastos, Michael","first_name":"Michael","last_name":"Anastos","id":"0b2a4358-bb35-11ec-b7b9-e3279b593dbb"}],"oa":1,"_id":"14867","type":"conference","language":[{"iso":"eng"}],"doi":"10.5817/cz.muni.eurocomb23-005","publisher":"Masaryk University Press","year":"2023","month":"09","quality_controlled":"1","publication_identifier":{"eissn":["2788-3116"]},"ddc":["510"],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"start_date":"2023-08-28","end_date":"2023-09-01","name":"EUROCOMB: European Conference on Combinatorics, Graph Theory and Applications","location":"Prague, Czech Republic"},"file_date_updated":"2024-01-24T09:34:43Z","date_created":"2024-01-22T12:20:15Z","acknowledgement":"This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034413.\r\n","department":[{"_id":"MaKw"}]},{"status":"public","type":"conference","doi":"10.1364/ls.2023.lm1f.3","language":[{"iso":"eng"}],"year":"2023","date_updated":"2024-01-24T08:43:28Z","publisher":"Optica Publishing Group","day":"01","publication_status":"published","date_published":"2023-10-01T00:00:00Z","oa_version":"None","abstract":[{"text":"We entangled microwave and optical photons for the first time as verified by a measured two-mode vacuum squeezing of 0.7 dB. This electro-optic entanglement is the key resource needed to connect cryogenic quantum circuits.","lang":"eng"}],"author":[{"last_name":"Sahu","id":"47D26E34-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6264-2162","first_name":"Rishabh","full_name":"Sahu, Rishabh"},{"full_name":"Qiu, Liu","first_name":"Liu","last_name":"Qiu"},{"orcid":"0000-0001-9868-2166","id":"29705398-F248-11E8-B48F-1D18A9856A87","last_name":"Hease","full_name":"Hease, William J","first_name":"William J"},{"orcid":"0000-0003-1397-7876","id":"3770C838-F248-11E8-B48F-1D18A9856A87","last_name":"Arnold","first_name":"Georg M","full_name":"Arnold, Georg M"},{"last_name":"Minoguchi","first_name":"Yuri","full_name":"Minoguchi, Yuri"},{"first_name":"Peter","full_name":"Rabl, Peter","last_name":"Rabl"},{"first_name":"Johannes M","full_name":"Fink, Johannes M","orcid":"0000-0001-8112-028X","last_name":"Fink","id":"4B591CBA-F248-11E8-B48F-1D18A9856A87"}],"_id":"14872","conference":{"location":"Tacoma, WA, United States","end_date":"2023-10-12","start_date":"2023-10-09","name":"Laser Science"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","article_number":"LM1F.3","date_created":"2024-01-22T12:29:41Z","citation":{"ieee":"R. Sahu <i>et al.</i>, “Entangling microwaves and telecom wavelength light,” in <i>Frontiers in Optics + Laser Science 2023</i>, Tacoma, WA, United States, 2023.","chicago":"Sahu, Rishabh, Liu Qiu, William J Hease, Georg M Arnold, Yuri Minoguchi, Peter Rabl, and Johannes M Fink. “Entangling Microwaves and Telecom Wavelength Light.” In <i>Frontiers in Optics + Laser Science 2023</i>. Optica Publishing Group, 2023. <a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">https://doi.org/10.1364/ls.2023.lm1f.3</a>.","ama":"Sahu R, Qiu L, Hease WJ, et al. Entangling microwaves and telecom wavelength light. In: <i>Frontiers in Optics + Laser Science 2023</i>. Optica Publishing Group; 2023. doi:<a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">10.1364/ls.2023.lm1f.3</a>","mla":"Sahu, Rishabh, et al. “Entangling Microwaves and Telecom Wavelength Light.” <i>Frontiers in Optics + Laser Science 2023</i>, LM1F.3, Optica Publishing Group, 2023, doi:<a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">10.1364/ls.2023.lm1f.3</a>.","apa":"Sahu, R., Qiu, L., Hease, W. J., Arnold, G. M., Minoguchi, Y., Rabl, P., &#38; Fink, J. M. (2023). Entangling microwaves and telecom wavelength light. In <i>Frontiers in Optics + Laser Science 2023</i>. Tacoma, WA, United States: Optica Publishing Group. <a href=\"https://doi.org/10.1364/ls.2023.lm1f.3\">https://doi.org/10.1364/ls.2023.lm1f.3</a>","short":"R. Sahu, L. Qiu, W.J. Hease, G.M. Arnold, Y. Minoguchi, P. Rabl, J.M. Fink, in:, Frontiers in Optics + Laser Science 2023, Optica Publishing Group, 2023.","ista":"Sahu R, Qiu L, Hease WJ, Arnold GM, Minoguchi Y, Rabl P, Fink JM. 2023. Entangling microwaves and telecom wavelength light. Frontiers in Optics + Laser Science 2023. Laser Science, LM1F.3."},"department":[{"_id":"JoFi"}],"publication":"Frontiers in Optics + Laser Science 2023","title":"Entangling microwaves and telecom wavelength light","month":"10","publication_identifier":{"isbn":["9781957171296"]},"quality_controlled":"1"},{"day":"02","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)"},"date_published":"2023-12-02T00:00:00Z","oa_version":"None","abstract":[{"lang":"eng","text":"Code and data necessary to reproduce the simulations and data analyses reported in our manuscript: Tomé, D.F., Zhang, Y., Aida, T., Mosto, O., Lu, Y., Chen, M., Sadeh, S., Roy, D. S., Clopath, C. Dynamic and selective engrams emerge with memory consolidation. 2023."}],"author":[{"id":"0eed2d40-3d48-11ec-8d38-f789cc2e40b2","last_name":"Feitosa Tomé","first_name":"Douglas","full_name":"Feitosa Tomé, Douglas"}],"_id":"14892","oa":1,"status":"public","type":"research_data_reference","doi":"10.5281/ZENODO.10251087","year":"2023","publisher":"Zenodo","date_updated":"2024-01-29T09:22:01Z","title":"douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"14887"}]},"month":"12","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.10251087"}],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["570"],"has_accepted_license":"1","date_created":"2024-01-29T09:06:43Z","department":[{"_id":"TiVo"}],"citation":{"chicago":"Feitosa Tomé, Douglas. “Douglastome/Dynamic-Engrams: Dynamic and Selective Engrams Emerge with Memory Consolidation.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.10251087\">https://doi.org/10.5281/ZENODO.10251087</a>.","ieee":"D. Feitosa Tomé, “douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation.” Zenodo, 2023.","ista":"Feitosa Tomé D. 2023. douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.10251087\">10.5281/ZENODO.10251087</a>.","apa":"Feitosa Tomé, D. (2023). douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.10251087\">https://doi.org/10.5281/ZENODO.10251087</a>","short":"D. Feitosa Tomé, (2023).","mla":"Feitosa Tomé, Douglas. <i>Douglastome/Dynamic-Engrams: Dynamic and Selective Engrams Emerge with Memory Consolidation</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.10251087\">10.5281/ZENODO.10251087</a>.","ama":"Feitosa Tomé D. douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.10251087\">10.5281/ZENODO.10251087</a>"}},{"ddc":["550"],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrPe"}],"citation":{"ama":"Shaw T, Buri P, McCarthy M, Miles E, Pellicciotti F. Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.8277285\">10.5281/ZENODO.8277285</a>","short":"T. Shaw, P. Buri, M. McCarthy, E. Miles, F. Pellicciotti, (2023).","ista":"Shaw T, Buri P, McCarthy M, Miles E, Pellicciotti F. 2023. Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.8277285\">10.5281/ZENODO.8277285</a>.","apa":"Shaw, T., Buri, P., McCarthy, M., Miles, E., &#38; Pellicciotti, F. (2023). Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.8277285\">https://doi.org/10.5281/ZENODO.8277285</a>","mla":"Shaw, Thomas, et al. <i>Air Temperature and Near-Surface Meteorology Datasets on Three Swiss Glaciers - Extreme 2022 Summer</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.8277285\">10.5281/ZENODO.8277285</a>.","ieee":"T. Shaw, P. Buri, M. McCarthy, E. Miles, and F. Pellicciotti, “Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer.” Zenodo, 2023.","chicago":"Shaw, Thomas, Pascal Buri, Michael McCarthy, Evan Miles, and Francesca Pellicciotti. “Air Temperature and Near-Surface Meteorology Datasets on Three Swiss Glaciers - Extreme 2022 Summer.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.8277285\">https://doi.org/10.5281/ZENODO.8277285</a>."},"date_created":"2024-01-31T12:08:26Z","title":"Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer","related_material":{"record":[{"status":"public","id":"14885","relation":"used_in_publication"}]},"main_file_link":[{"url":"https://doi.org/10.5281/ZENODO.8277285","open_access":"1"}],"month":"08","type":"research_data_reference","status":"public","year":"2023","date_updated":"2024-02-06T08:44:01Z","publisher":"Zenodo","doi":"10.5281/ZENODO.8277285","abstract":[{"lang":"eng","text":"GLACIER METEOROLOGICAL DATA SWISS ALPS -2022\r\n"}],"day":"23","oa_version":"Published Version","date_published":"2023-08-23T00:00:00Z","_id":"14919","oa":1,"author":[{"first_name":"Thomas","full_name":"Shaw, Thomas","last_name":"Shaw","id":"3caa3f91-1f03-11ee-96ce-e0e553054d6e","orcid":"0000-0001-7640-6152"},{"full_name":"Buri, Pascal","first_name":"Pascal","id":"317987aa-9421-11ee-ac5a-b941b041abba","last_name":"Buri"},{"first_name":"Michael","full_name":"McCarthy, Michael","last_name":"McCarthy"},{"full_name":"Miles, Evan","first_name":"Evan","last_name":"Miles"},{"full_name":"Pellicciotti, Francesca","first_name":"Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","last_name":"Pellicciotti","orcid":"0000-0002-5554-8087"}]},{"publication_status":"published","oa_version":"Published Version","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":"24","ec_funded":1,"abstract":[{"text":"We consider fixpoint algorithms for two-player games on graphs with $\\omega$-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness, which requires that any execution is strongly fair with respect to a specified set of live edges: whenever the\r\nsource vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well. We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for $\\omega$-regular games -- the new algorithms have the same alternation depth as the classical algorithms but invoke a new type of predecessor operator. For Rabin games with $k$ pairs, the complexity of the new algorithm is $O(n^{k+2}k!)$ symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. Further, we show that GR(1) specifications with strong transition fairness assumptions can be solved with a 3-nested fixpoint algorithm, same as the usual algorithm. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions. We get symbolic algorithms for (generalized) Rabin, parity and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic\r\n$\\omega$-regular games that runs in $O(n^{k+2}k!)$ symbolic steps, improving the state of the art. Finally, we have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude.","lang":"eng"}],"status":"public","date_updated":"2024-02-05T10:21:51Z","publication":"TheoretiCS","external_id":{"arxiv":["2202.07480"]},"file":[{"file_name":"2023_TheoretiCS_Banerjee.pdf","file_size":917076,"file_id":"14940","date_created":"2024-02-05T10:19:35Z","checksum":"2972d531122a6f15727b396110fb3f5c","date_updated":"2024-02-05T10:19:35Z","success":1,"creator":"dernst","access_level":"open_access","relation":"main_file","content_type":"application/pdf"}],"title":"Fast symbolic algorithms for mega-regular games under strong transition fairness","has_accepted_license":"1","project":[{"grant_number":"101020093","call_identifier":"H2020","name":"Vigilant Algorithmic Monitoring of Software","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"}],"citation":{"chicago":"Banerjee, Tamajit, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck, and Sadegh Soudjani. “Fast Symbolic Algorithms for Mega-Regular Games under Strong Transition Fairness.” <i>TheoretiCS</i>. EPI Sciences, 2023. <a href=\"https://doi.org/10.46298/theoretics.23.4\">https://doi.org/10.46298/theoretics.23.4</a>.","ieee":"T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, and S. Soudjani, “Fast symbolic algorithms for mega-regular games under strong transition fairness,” <i>TheoretiCS</i>, vol. 2. EPI Sciences, 2023.","apa":"Banerjee, T., Majumdar, R., Mallik, K., Schmuck, A.-K., &#38; Soudjani, S. (2023). Fast symbolic algorithms for mega-regular games under strong transition fairness. <i>TheoretiCS</i>. EPI Sciences. <a href=\"https://doi.org/10.46298/theoretics.23.4\">https://doi.org/10.46298/theoretics.23.4</a>","ista":"Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. 2023. Fast symbolic algorithms for mega-regular games under strong transition fairness. TheoretiCS. 2, 4.","mla":"Banerjee, Tamajit, et al. “Fast Symbolic Algorithms for Mega-Regular Games under Strong Transition Fairness.” <i>TheoretiCS</i>, vol. 2, 4, EPI Sciences, 2023, doi:<a href=\"https://doi.org/10.46298/theoretics.23.4\">10.46298/theoretics.23.4</a>.","short":"T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, S. Soudjani, TheoretiCS 2 (2023).","ama":"Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. Fast symbolic algorithms for mega-regular games under strong transition fairness. <i>TheoretiCS</i>. 2023;2. doi:<a href=\"https://doi.org/10.46298/theoretics.23.4\">10.46298/theoretics.23.4</a>"},"arxiv":1,"date_published":"2023-02-24T00:00:00Z","author":[{"first_name":"Tamajit","full_name":"Banerjee, Tamajit","last_name":"Banerjee"},{"last_name":"Majumdar","first_name":"Rupak","full_name":"Majumdar, Rupak"},{"full_name":"Mallik, Kaushik","first_name":"Kaushik","orcid":"0000-0001-9864-7475","id":"0834ff3c-6d72-11ec-94e0-b5b0a4fb8598","last_name":"Mallik"},{"last_name":"Schmuck","first_name":"Anne-Kathrin","full_name":"Schmuck, Anne-Kathrin"},{"last_name":"Soudjani","first_name":"Sadegh","full_name":"Soudjani, Sadegh"}],"oa":1,"_id":"14920","type":"journal_article","language":[{"iso":"eng"}],"doi":"10.46298/theoretics.23.4","publisher":"EPI Sciences","year":"2023","article_type":"original","volume":2,"month":"02","quality_controlled":"1","publication_identifier":{"issn":["2751-4838"]},"article_processing_charge":"Yes","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["000"],"file_date_updated":"2024-02-05T10:19:35Z","intvolume":"         2","article_number":"4","date_created":"2024-01-31T13:40:49Z","department":[{"_id":"ToHe"}],"acknowledgement":"A previous version of this paper has appeared in TACAS 2022. Authors ordered alphabetically. T. Banerjee was interning with MPI-SWS when this research was conducted. R. Majumdar and A.-K. Schmuck are partially supported by DFG project 389792660 TRR 248–CPEC. A.-K. Schmuck is additionally funded through DFG project (SCHM 3541/1-1). K. Mallik is supported by the ERC project ERC-2020-AdG 101020093."},{"date_published":"2023-12-15T00:00:00Z","publication_status":"inpress","oa_version":"Preprint","day":"15","abstract":[{"lang":"eng","text":"Neural collapse (NC) refers to the surprising structure of the last layer of deep neural networks in the terminal phase of gradient descent training. Recently, an increasing amount of experimental evidence has pointed to the propagation of NC to earlier layers of neural networks. However, while the NC in the last layer is well studied theoretically, much less is known about its multi-layered counterpart - deep neural collapse (DNC). In particular, existing work focuses either on linear layers or only on the last two layers at the price of an extra assumption. Our paper fills this gap by generalizing the established analytical framework for NC - the unconstrained features model - to multiple non-linear layers. Our key technical contribution is to show that, in a deep unconstrained features model, the unique global optimum for binary classification exhibits all the properties typical of DNC. This explains the existing experimental evidence of DNC. We also empirically show that (i) by optimizing deep unconstrained features models via gradient descent, the resulting solution agrees well with our theory, and (ii) trained networks recover the unconstrained features suitable for the occurrence of DNC, thus supporting the validity of this modeling principle."}],"author":[{"full_name":"Súkeník, Peter","first_name":"Peter","id":"d64d6a8d-eb8e-11eb-b029-96fd216dec3c","last_name":"Súkeník"},{"full_name":"Mondelli, Marco","first_name":"Marco","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","orcid":"0000-0002-3242-7020"},{"first_name":"Christoph","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"oa":1,"_id":"14921","status":"public","type":"conference","language":[{"iso":"eng"}],"date_updated":"2024-09-10T13:03:19Z","year":"2023","publication":"37th Annual Conference on Neural Information Processing Systems","external_id":{"arxiv":["2305.13165"]},"title":"Deep neural collapse is provably optimal for the deep unconstrained features model","month":"12","quality_controlled":"1","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2305.13165","open_access":"1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","conference":{"location":"New Orleans, LA, United States","name":"NeurIPS: Neural Information Processing Systems","end_date":"2023-12-16","start_date":"2023-12-10"},"alternative_title":["NeurIPS"],"date_created":"2024-02-02T11:17:41Z","project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"citation":{"apa":"Súkeník, P., Mondelli, M., &#38; Lampert, C. (n.d.). Deep neural collapse is provably optimal for the deep unconstrained features model. In <i>37th Annual Conference on Neural Information Processing Systems</i>. New Orleans, LA, United States.","mla":"Súkeník, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” <i>37th Annual Conference on Neural Information Processing Systems</i>.","short":"P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, n.d.","ista":"Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. 37th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, NeurIPS, .","ama":"Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. In: <i>37th Annual Conference on Neural Information Processing Systems</i>.","chicago":"Súkeník, Peter, Marco Mondelli, and Christoph Lampert. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” In <i>37th Annual Conference on Neural Information Processing Systems</i>, n.d.","ieee":"P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably optimal for the deep unconstrained features model,” in <i>37th Annual Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States."},"arxiv":1,"acknowledgement":"M. M. is partially supported by the 2019 Lopez-Loreta Prize. The authors would like to thank Eugenia Iofinova, Bernd Prach and Simone Bombari for valuable feedback on the manuscript.","department":[{"_id":"MaMo"},{"_id":"ChLa"}]},{"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"start_date":"2023-06-25","end_date":"2023-06-30","name":"ISIT: IEEE International Symposium on Information Theory","location":"Taipei, Taiwan"},"date_created":"2024-02-02T11:18:40Z","project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"acknowledgement":"The authors are partially supported by the 2019 Lopez-Loreta Prize. They would also like to thank Professor Jan Maas for providing valuable suggestions and comments on an early version of the work.","citation":{"ama":"Esposito AR, Mondelli M. Concentration without independence via information measures. In: <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. IEEE. doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">10.1109/isit54713.2023.10206899</a>","apa":"Esposito, A. R., &#38; Mondelli, M. (n.d.). Concentration without independence via information measures. In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. Taipei, Taiwan: IEEE. <a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">https://doi.org/10.1109/isit54713.2023.10206899</a>","short":"A.R. Esposito, M. Mondelli, in:, Proceedings of 2023 IEEE International Symposium on Information Theory, IEEE, n.d.","mla":"Esposito, Amedeo Roberto, and Marco Mondelli. “Concentration without Independence via Information Measures.” <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, IEEE, doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">10.1109/isit54713.2023.10206899</a>.","ista":"Esposito AR, Mondelli M. Concentration without independence via information measures. Proceedings of 2023 IEEE International Symposium on Information Theory. ISIT: IEEE International Symposium on Information Theory.","ieee":"A. R. Esposito and M. Mondelli, “Concentration without independence via information measures,” in <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, Taipei, Taiwan.","chicago":"Esposito, Amedeo Roberto, and Marco Mondelli. “Concentration without Independence via Information Measures.” In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. IEEE, n.d. <a href=\"https://doi.org/10.1109/isit54713.2023.10206899\">https://doi.org/10.1109/isit54713.2023.10206899</a>."},"arxiv":1,"department":[{"_id":"MaMo"}],"publication":"Proceedings of 2023 IEEE International Symposium on Information Theory","external_id":{"arxiv":["2303.07245"]},"title":"Concentration without independence via information measures","month":"06","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2303.07245"}],"status":"public","type":"conference","language":[{"iso":"eng"}],"doi":"10.1109/isit54713.2023.10206899","date_updated":"2024-02-14T14:24:25Z","publisher":"IEEE","year":"2023","oa_version":"Preprint","publication_status":"inpress","date_published":"2023-06-30T00:00:00Z","day":"30","abstract":[{"text":"We propose a novel approach to concentration for non-independent random variables. The main idea is to ``pretend'' that the random variables are independent and pay a multiplicative price measuring how far they are from actually being independent. This price is encapsulated in the Hellinger integral between the joint and the product of the marginals, which is then upper bounded leveraging tensorisation properties. Our bounds represent a natural generalisation of concentration inequalities in the presence of dependence: we recover exactly the classical bounds (McDiarmid's inequality) when the random variables are independent. Furthermore, in a ``large deviations'' regime, we obtain the same decay in the probability as for the independent case, even when the random variables display non-trivial dependencies. To show this, we consider a number of applications of interest. First, we provide a bound for Markov chains with finite state space. Then, we consider the Simple Symmetric Random Walk, which is a non-contracting Markov chain, and a non-Markovian setting in which the stochastic process depends on its entire past. To conclude, we propose an application to Markov Chain Monte Carlo methods, where our approach leads to an improved lower bound on the minimum burn-in period required to reach a certain accuracy. In all of these settings, we provide a regime of parameters in which our bound fares better than what the state of the art can provide.","lang":"eng"}],"author":[{"id":"9583e921-e1ad-11ec-9862-cef099626dc9","last_name":"Esposito","full_name":"Esposito, Amedeo Roberto","first_name":"Amedeo Roberto"},{"first_name":"Marco","full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425"}],"oa":1,"_id":"14922"},{"title":"Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise","external_id":{"arxiv":["2302.03306"]},"publication":"Proceedings of 2023 IEEE International Symposium on Information Theory","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2302.03306"}],"quality_controlled":"1","month":"06","conference":{"name":"ISIT: IEEE International Symposium on Information Theory","start_date":"2023-06-25","end_date":"2023-06-30","location":"Taipei, Taiwan"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","citation":{"mla":"Fu, Teng, et al. “Mismatched Estimation of Non-Symmetric Rank-One Matrices Corrupted by Structured Noise.” <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, IEEE, doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">10.1109/isit54713.2023.10206671</a>.","apa":"Fu, T., Liu, Y., Barbier, J., Mondelli, M., Liang, S., &#38; Hou, T. (n.d.). Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. Taipei, Taiwan: IEEE. <a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">https://doi.org/10.1109/isit54713.2023.10206671</a>","ista":"Fu T, Liu Y, Barbier J, Mondelli M, Liang S, Hou T. Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. Proceedings of 2023 IEEE International Symposium on Information Theory. ISIT: IEEE International Symposium on Information Theory.","short":"T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, T. Hou, in:, Proceedings of 2023 IEEE International Symposium on Information Theory, IEEE, n.d.","ama":"Fu T, Liu Y, Barbier J, Mondelli M, Liang S, Hou T. Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In: <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. IEEE. doi:<a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">10.1109/isit54713.2023.10206671</a>","chicago":"Fu, Teng, YuHao Liu, Jean Barbier, Marco Mondelli, ShanSuo Liang, and TianQi Hou. “Mismatched Estimation of Non-Symmetric Rank-One Matrices Corrupted by Structured Noise.” In <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>. IEEE, n.d. <a href=\"https://doi.org/10.1109/isit54713.2023.10206671\">https://doi.org/10.1109/isit54713.2023.10206671</a>.","ieee":"T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, and T. Hou, “Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise,” in <i>Proceedings of 2023 IEEE International Symposium on Information Theory</i>, Taipei, Taiwan."},"department":[{"_id":"MaMo"}],"arxiv":1,"date_created":"2024-02-02T11:20:39Z","abstract":[{"lang":"eng","text":"We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular values. As the signal-to-noise ratio and the noise structure are unknown, a Gaussian setup is incorrectly assumed. We derive the exact analytic expression for the error of the mismatched Bayes estimator and also provide the analysis of an approximate message passing (AMP) algorithm. The first result exploits the asymptotic behavior of spherical integrals for rectangular matrices and of low-rank matrix perturbations; the second one relies on the design and analysis of an auxiliary AMP. The numerical experiments show that there is a performance gap between the AMP and Bayes estimators, which is due to the incorrect estimation of the signal norm."}],"day":"30","publication_status":"inpress","date_published":"2023-06-30T00:00:00Z","oa_version":"Preprint","_id":"14923","oa":1,"author":[{"first_name":"Teng","full_name":"Fu, Teng","last_name":"Fu"},{"first_name":"YuHao","full_name":"Liu, YuHao","last_name":"Liu"},{"last_name":"Barbier","full_name":"Barbier, Jean","first_name":"Jean"},{"first_name":"Marco","full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425"},{"last_name":"Liang","first_name":"ShanSuo","full_name":"Liang, ShanSuo"},{"first_name":"TianQi","full_name":"Hou, TianQi","last_name":"Hou"}],"type":"conference","status":"public","year":"2023","publisher":"IEEE","date_updated":"2024-02-14T14:34:03Z","doi":"10.1109/isit54713.2023.10206671","language":[{"iso":"eng"}]},{"publication":"Transactions on Machine Learning Research","title":"Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence","external_id":{"arxiv":["2210.06819"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2210.06819"}],"has_accepted_license":"1","alternative_title":["TMLR"],"citation":{"ieee":"D. Wu, V. Kungurtsev, and M. Mondelli, “Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence,” in <i>Transactions on Machine Learning Research</i>, 2023.","chicago":"Wu, Diyuan, Vyacheslav Kungurtsev, and Marco Mondelli. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.” In <i>Transactions on Machine Learning Research</i>. ML Research Press, 2023.","ama":"Wu D, Kungurtsev V, Mondelli M. Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In: <i>Transactions on Machine Learning Research</i>. ML Research Press; 2023.","apa":"Wu, D., Kungurtsev, V., &#38; Mondelli, M. (2023). Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In <i>Transactions on Machine Learning Research</i>. ML Research Press.","short":"D. Wu, V. Kungurtsev, M. Mondelli, in:, Transactions on Machine Learning Research, ML Research Press, 2023.","ista":"Wu D, Kungurtsev V, Mondelli M. 2023. Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. Transactions on Machine Learning Research. , TMLR, .","mla":"Wu, Diyuan, et al. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.” <i>Transactions on Machine Learning Research</i>, ML Research Press, 2023."},"arxiv":1,"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"day":"28","oa_version":"Published Version","publication_status":"published","abstract":[{"text":"The stochastic heavy ball method (SHB), also known as stochastic gradient descent (SGD) with Polyak's momentum, is widely used in training neural networks. However, despite the remarkable success of such algorithm in practice, its theoretical characterization remains limited. In this paper, we focus on neural networks with two and three layers and provide a rigorous understanding of the properties of the solutions found by SHB: \\emph{(i)} stability after dropping out part of the neurons, \\emph{(ii)} connectivity along a low-loss path, and \\emph{(iii)} convergence to the global optimum.\r\nTo achieve this goal, we take a mean-field view and relate the SHB dynamics to a certain partial differential equation in the limit of large network widths. This mean-field perspective has inspired a recent line of work focusing on SGD while, in contrast, our paper considers an algorithm with momentum. More specifically, after proving existence and uniqueness of the limit differential equations, we show convergence to the global optimum and give a quantitative bound between the mean-field limit and the SHB dynamics of a finite-width network. Armed with this last bound, we are able to establish the dropout-stability and connectivity of SHB solutions.","lang":"eng"}],"status":"public","date_updated":"2024-09-10T13:03:20Z","month":"02","quality_controlled":"1","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2024-02-02T11:21:56Z","department":[{"_id":"MaMo"}],"acknowledgement":"D. Wu and M. Mondelli are partially supported by the 2019 Lopez-Loreta Prize. V. Kungurtsev was supported by the OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 \"Research Center for Informatics\".","date_published":"2023-02-28T00:00:00Z","author":[{"id":"1a5914c2-896a-11ed-bdf8-fb80621a0635","last_name":"Wu","first_name":"Diyuan","full_name":"Wu, Diyuan"},{"first_name":"Vyacheslav","full_name":"Kungurtsev, Vyacheslav","last_name":"Kungurtsev"},{"last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","orcid":"0000-0002-3242-7020","full_name":"Mondelli, Marco","first_name":"Marco"}],"_id":"14924","oa":1,"type":"conference","language":[{"iso":"eng"}],"year":"2023","publisher":"ML Research Press"},{"external_id":{"arxiv":["2311.04056"]},"title":"Multi-view causal representation learning with partial observability","publication":"arXiv","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2311.04056","open_access":"1"}],"month":"11","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"This work was initiated at the Second Bellairs Workshop on Causality held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop participants for providing a stimulating research environment. Further, we thank Cian Eastwood, Luigi Gresele, Stefano Soatto, Marco Bagatella, and A. René Geist for helpful discussion. GM is a member of the Machine Learning Cluster of Excellence, EXC number 2064/1 – Project number 390727645. JvK and GM acknowledge support from the German Federal Ministry of Education and Research (BMBF) through the Tübingen AI Center (FKZ: 01IS18039B). The research of DX and SM was supported by the Air Force Office of Scientific Research under award number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations expressed in\r\nthis material are those of the author(s) and do not necessarily reflect the views of the United States Air Force. We also thank SURF for the support in using the Dutch National Supercomputer Snellius. DY was supported by an Amazon fellowship and the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Work done outside of Amazon. SL was supported by an IVADO excellence PhD scholarship and by Samsung Electronics Co., Ldt.","citation":{"chicago":"Yao, Dingling, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, and Francesco Locatello. “Multi-View Causal Representation Learning with Partial Observability.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2311.04056\">https://doi.org/10.48550/arXiv.2311.04056</a>.","ieee":"D. Yao <i>et al.</i>, “Multi-view causal representation learning with partial observability,” <i>arXiv</i>. .","mla":"Yao, Dingling, et al. “Multi-View Causal Representation Learning with Partial Observability.” <i>ArXiv</i>, 2311.04056, doi:<a href=\"https://doi.org/10.48550/arXiv.2311.04056\">10.48550/arXiv.2311.04056</a>.","short":"D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, ArXiv (n.d.).","apa":"Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G., … Locatello, F. (n.d.). Multi-view causal representation learning with partial observability. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2311.04056\">https://doi.org/10.48550/arXiv.2311.04056</a>","ista":"Yao D, Xu D, Lachapelle S, Magliacane S, Taslakian P, Martius G, Kügelgen J von, Locatello F. Multi-view causal representation learning with partial observability. arXiv, 2311.04056.","ama":"Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning with partial observability. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2311.04056\">10.48550/arXiv.2311.04056</a>"},"department":[{"_id":"FrLo"}],"arxiv":1,"date_created":"2024-02-07T14:28:34Z","article_number":"2311.04056","abstract":[{"lang":"eng","text":"We present a unified framework for studying the identifiability of\r\nrepresentations learned from simultaneously observed views, such as different\r\ndata modalities. We allow a partially observed setting in which each view\r\nconstitutes a nonlinear mixture of a subset of underlying latent variables,\r\nwhich can be causally related. We prove that the information shared across all\r\nsubsets of any number of views can be learned up to a smooth bijection using\r\ncontrastive learning and a single encoder per view. We also provide graphical\r\ncriteria indicating which latent variables can be identified through a simple\r\nset of rules, which we refer to as identifiability algebra. Our general\r\nframework and theoretical results unify and extend several previous works on\r\nmulti-view nonlinear ICA, disentanglement, and causal representation learning.\r\nWe experimentally validate our claims on numerical, image, and multi-modal data\r\nsets. Further, we demonstrate that the performance of prior methods is\r\nrecovered in different special cases of our setup. Overall, we find that access\r\nto multiple partial views enables us to identify a more fine-grained\r\nrepresentation, under the generally milder assumption of partial observability."}],"oa_version":"Preprint","publication_status":"submitted","date_published":"2023-11-07T00:00:00Z","day":"07","oa":1,"_id":"14946","author":[{"id":"d3e02e50-48a8-11ee-8f62-c108061797fa","last_name":"Yao","full_name":"Yao, Dingling","first_name":"Dingling"},{"last_name":"Xu","full_name":"Xu, Danru","first_name":"Danru"},{"full_name":"Lachapelle, Sébastien","first_name":"Sébastien","last_name":"Lachapelle"},{"first_name":"Sara","full_name":"Magliacane, Sara","last_name":"Magliacane"},{"full_name":"Taslakian, Perouz","first_name":"Perouz","last_name":"Taslakian"},{"last_name":"Martius","full_name":"Martius, Georg","first_name":"Georg"},{"last_name":"Kügelgen","full_name":"Kügelgen, Julius von","first_name":"Julius von"},{"first_name":"Francesco","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello"}],"type":"preprint","status":"public","date_updated":"2024-02-12T08:07:33Z","year":"2023","language":[{"iso":"eng"}],"doi":"10.48550/arXiv.2311.04056"},{"abstract":[{"lang":"eng","text":"The extraction of modular object-centric representations for downstream tasks\r\nis an emerging area of research. Learning grounded representations of objects\r\nthat are guaranteed to be stable and invariant promises robust performance\r\nacross different tasks and environments. Slot Attention (SA) learns\r\nobject-centric representations by assigning objects to \\textit{slots}, but\r\npresupposes a \\textit{single} distribution from which all slots are randomly\r\ninitialised. This results in an inability to learn \\textit{specialized} slots\r\nwhich bind to specific object types and remain invariant to identity-preserving\r\nchanges in object appearance. To address this, we present\r\n\\emph{\\textsc{Co}nditional \\textsc{S}lot \\textsc{A}ttention} (\\textsc{CoSA})\r\nusing a novel concept of \\emph{Grounded Slot Dictionary} (GSD) inspired by\r\nvector quantization. Our proposed GSD comprises (i) canonical object-level\r\nproperty vectors and (ii) parametric Gaussian distributions, which define a\r\nprior over the slots. We demonstrate the benefits of our method in multiple\r\ndownstream tasks such as scene generation, composition, and task adaptation,\r\nwhilst remaining competitive with SA in popular object discovery benchmarks."}],"publication_status":"submitted","date_published":"2023-07-18T00:00:00Z","oa_version":"Preprint","day":"18","oa":1,"_id":"14948","author":[{"last_name":"Kori","first_name":"Avinash","full_name":"Kori, Avinash"},{"last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco"},{"last_name":"Ribeiro","first_name":"Fabio De Sousa","full_name":"Ribeiro, Fabio De Sousa"},{"last_name":"Toni","full_name":"Toni, Francesca","first_name":"Francesca"},{"full_name":"Glocker, Ben","first_name":"Ben","last_name":"Glocker"}],"type":"preprint","status":"public","date_updated":"2024-02-12T08:13:12Z","year":"2023","language":[{"iso":"eng"}],"doi":"10.48550/arXiv.2307.09437","external_id":{"arxiv":["2307.09437"]},"title":"Grounded object centric learning","publication":"arXiv","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2307.09437","open_access":"1"}],"month":"07","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","department":[{"_id":"FrLo"}],"arxiv":1,"citation":{"apa":"Kori, A., Locatello, F., Ribeiro, F. D. S., Toni, F., &#38; Glocker, B. (n.d.). Grounded object centric learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2307.09437\">https://doi.org/10.48550/arXiv.2307.09437</a>","short":"A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).","ista":"Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. arXiv, 2307.09437.","mla":"Kori, Avinash, et al. “Grounded Object Centric Learning.” <i>ArXiv</i>, 2307.09437, doi:<a href=\"https://doi.org/10.48550/arXiv.2307.09437\">10.48550/arXiv.2307.09437</a>.","ama":"Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2307.09437\">10.48550/arXiv.2307.09437</a>","chicago":"Kori, Avinash, Francesco Locatello, Fabio De Sousa Ribeiro, Francesca Toni, and Ben Glocker. “Grounded Object Centric Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2307.09437\">https://doi.org/10.48550/arXiv.2307.09437</a>.","ieee":"A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded object centric learning,” <i>arXiv</i>. ."},"acknowledgement":"This work was supported by supported by UKRI (grant agreement no. EP/S023356/1), in the UKRI\r\nCentre for Doctoral Training in Safe and Trusted AI via A. Kori.","article_number":"2307.09437","date_created":"2024-02-07T14:47:04Z"},{"article_type":"original","month":"12","quality_controlled":"1","publication_identifier":{"eissn":["2835-8856"]},"ddc":["000"],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2024-02-07T14:57:32Z","date_created":"2024-02-07T14:57:39Z","acknowledgement":"The authors would like to thank Varad Gunjal and Vishaal Udandarao. MFB thanks the International Max Planck Research School for Intelligent Systems (IMPRS-IS).","department":[{"_id":"FrLo"}],"date_published":"2023-12-10T00:00:00Z","author":[{"last_name":"Burg","first_name":"Max","full_name":"Burg, Max"},{"last_name":"Wenzel","first_name":"Florian","full_name":"Wenzel, Florian"},{"last_name":"Zietlow","first_name":"Dominik","full_name":"Zietlow, Dominik"},{"last_name":"Horn","full_name":"Horn, Max","first_name":"Max"},{"full_name":"Makansi, Osama","first_name":"Osama","last_name":"Makansi"},{"full_name":"Locatello, Francesco","first_name":"Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"first_name":"Chris","full_name":"Russell, Chris","last_name":"Russell"}],"oa":1,"_id":"14949","type":"journal_article","language":[{"iso":"eng"}],"publisher":"ML Research Press","year":"2023","publication":"Journal of Machine Learning Research","title":"Image retrieval outperforms diffusion models on data augmentation","file":[{"date_created":"2024-02-07T14:57:32Z","checksum":"af87ddea7908923426365347b9c87ba7","file_id":"14950","date_updated":"2024-02-07T14:57:32Z","file_name":"Burg_et_al_2023_Image_retrieval_outperforms.pdf","file_size":27325153,"access_level":"open_access","relation":"main_file","content_type":"application/pdf","creator":"ptazenko"}],"main_file_link":[{"url":"https://openreview.net/forum?id=xflYdGZMpv","open_access":"1"}],"alternative_title":["TMLR"],"has_accepted_license":"1","citation":{"ama":"Burg M, Wenzel F, Zietlow D, et al. Image retrieval outperforms diffusion models on data augmentation. <i>Journal of Machine Learning Research</i>. 2023.","apa":"Burg, M., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &#38; Russell, C. (2023). Image retrieval outperforms diffusion models on data augmentation. <i>Journal of Machine Learning Research</i>. ML Research Press.","ista":"Burg M, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. 2023. Image retrieval outperforms diffusion models on data augmentation. Journal of Machine Learning Research.","mla":"Burg, Max, et al. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” <i>Journal of Machine Learning Research</i>, ML Research Press, 2023.","short":"M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, Journal of Machine Learning Research (2023).","ieee":"M. Burg <i>et al.</i>, “Image retrieval outperforms diffusion models on data augmentation,” <i>Journal of Machine Learning Research</i>. ML Research Press, 2023.","chicago":"Burg, Max, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” <i>Journal of Machine Learning Research</i>. ML Research Press, 2023."},"publication_status":"published","oa_version":"Published Version","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":"10","abstract":[{"lang":"eng","text":"Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large datasets, often with noisy annotations, and it remains an open question to which extent these models contribute to downstream classification performance. In particular, it remains unclear if they generalize enough to improve over directly using the additional data of their pre-training process for augmentation. We systematically evaluate a range of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. Personalizing diffusion models towards the target data outperforms simpler prompting strategies. However, using the pre-training data of the diffusion model alone, via a simple nearest-neighbor retrieval procedure, leads to even stronger downstream performance. Our study explores the potential of diffusion models in generating new training data, and surprisingly finds that these sophisticated models are not yet able to beat a simple and strong image retrieval baseline on simple downstream vision tasks."}],"status":"public","date_updated":"2024-02-12T08:30:21Z"},{"article_number":"2311.00664","date_created":"2024-02-07T15:08:55Z","citation":{"ama":"Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2311.00664\">10.48550/arXiv.2311.00664</a>","short":"V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, E. Rodolà, ArXiv (n.d.).","ista":"Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. arXiv, 2311.00664.","mla":"Maiorca, Valentino, et al. “Latent Space Translation via Semantic Alignment.” <i>ArXiv</i>, 2311.00664, doi:<a href=\"https://doi.org/10.48550/arXiv.2311.00664\">10.48550/arXiv.2311.00664</a>.","apa":"Maiorca, V., Moschella, L., Norelli, A., Fumero, M., Locatello, F., &#38; Rodolà, E. (n.d.). Latent space translation via semantic alignment. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2311.00664\">https://doi.org/10.48550/arXiv.2311.00664</a>","ieee":"V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, and E. Rodolà, “Latent space translation via semantic alignment,” <i>arXiv</i>. .","chicago":"Maiorca, Valentino, Luca Moschella, Antonio Norelli, Marco Fumero, Francesco Locatello, and Emanuele Rodolà. “Latent Space Translation via Semantic Alignment.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2311.00664\">https://doi.org/10.48550/arXiv.2311.00664</a>."},"acknowledgement":"This work is supported by the ERC grant no.802554 (SPECGEO), PRIN 2020 project no.2020TA3K9N (LEGO.AI), and PNRR MUR project PE0000013-FAIR. Francesco\r\nLocatello did not contribute to this work at Amazon.","arxiv":1,"department":[{"_id":"FrLo"}],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"11","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2311.00664","open_access":"1"}],"publication":"arXiv","title":"Latent space translation via semantic alignment","external_id":{"arxiv":["2311.00664"]},"doi":"10.48550/arXiv.2311.00664","language":[{"iso":"eng"}],"year":"2023","date_updated":"2024-02-12T09:40:23Z","status":"public","type":"preprint","author":[{"last_name":"Maiorca","full_name":"Maiorca, Valentino","first_name":"Valentino"},{"last_name":"Moschella","first_name":"Luca","full_name":"Moschella, Luca"},{"last_name":"Norelli","full_name":"Norelli, Antonio","first_name":"Antonio"},{"first_name":"Marco","full_name":"Fumero, Marco","last_name":"Fumero"},{"last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco"},{"first_name":"Emanuele","full_name":"Rodolà, Emanuele","last_name":"Rodolà"}],"_id":"14952","oa":1,"day":"01","oa_version":"Preprint","date_published":"2023-11-01T00:00:00Z","publication_status":"submitted","abstract":[{"text":"While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible. Towards a better understanding of this phenomenon, our work shows how representations learned from these neural modules can be translated between different pre-trained networks via simpler transformations than previously thought. An advantage of this approach is the ability to\r\nestimate these transformations using standard, well-understood algebraic procedures that have closed-form solutions. Our method directly estimates a transformation between two given latent spaces, thereby enabling effective stitching of encoders and decoders without additional training. We extensively validate the adaptability of this translation procedure in different\r\nexperimental settings: across various trainings, domains, architectures (e.g., ResNet, CNN, ViT), and in multiple downstream tasks (classification, reconstruction). Notably, we show how it is possible to zero-shot stitch text encoders and vision decoders, or vice-versa, yielding surprisingly good classification performance in this multimodal setting.","lang":"eng"}]},{"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"We are thankful to the reviewers for providing constructive feedback and Kun Zhang and Dominik Janzing for helpful discussion on the special case of deterministic children. This work was supported by Hasler Foundation Program: Hasler Responsible AI (project number 21043). This work was supported by the Swiss National Science Foundation (SNSF) under grant number 200021_205011. Francesco Locatello did not contribute to this work at Amazon. ","department":[{"_id":"FrLo"}],"arxiv":1,"citation":{"chicago":"Zhu, Zhenyu, Francesco Locatello, and Volkan Cevher. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2310.18123\">https://doi.org/10.48550/arXiv.2310.18123</a>.","ieee":"Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” <i>arXiv</i>. .","short":"Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).","ista":"Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv, 2310.18123.","apa":"Zhu, Z., Locatello, F., &#38; Cevher, V. (n.d.). Sample complexity bounds for score-matching: Causal discovery and generative modeling. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2310.18123\">https://doi.org/10.48550/arXiv.2310.18123</a>","mla":"Zhu, Zhenyu, et al. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.” <i>ArXiv</i>, 2310.18123, doi:<a href=\"https://doi.org/10.48550/arXiv.2310.18123\">10.48550/arXiv.2310.18123</a>.","ama":"Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2310.18123\">10.48550/arXiv.2310.18123</a>"},"article_number":"2310.18123","date_created":"2024-02-07T15:11:11Z","external_id":{"arxiv":["2310.18123"]},"title":"Sample complexity bounds for score-matching: Causal discovery and generative modeling","publication":"arXiv","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.18123","open_access":"1"}],"month":"10","type":"preprint","status":"public","date_updated":"2024-02-12T09:45:58Z","year":"2023","language":[{"iso":"eng"}],"doi":"10.48550/arXiv.2310.18123","abstract":[{"lang":"eng","text":"This paper provides statistical sample complexity bounds for score-matching and its applications in causal discovery. We demonstrate that accurate estimation of the score function is achievable by training a standard deep ReLU neural network using stochastic gradient descent. We establish bounds on the error rate of recovering causal relationships using the score-matching-based causal discovery method of Rolland et al. [2022], assuming a sufficiently good estimation of the score function. Finally, we analyze the upper bound of score-matching estimation within the score-based generative modeling, which has been applied for causal discovery but is also of independent interest within the domain of generative models."}],"date_published":"2023-10-27T00:00:00Z","publication_status":"submitted","oa_version":"Preprint","day":"27","oa":1,"_id":"14953","author":[{"full_name":"Zhu, Zhenyu","first_name":"Zhenyu","last_name":"Zhu"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco"},{"first_name":"Volkan","full_name":"Cevher, Volkan","last_name":"Cevher"}]}]