--- _id: '12538' abstract: - lang: eng text: In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with MIEEE Information Theory Workshop. 2022:588-593. doi:10.1109/ITW54588.2022.9965870 apa: 'Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., & Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop. Mumbai, India: IEEE. https://doi.org/10.1109/ITW54588.2022.9965870' chicago: Amani, Mohammad Hossein, Simone Bombari, Marco Mondelli, Rattana Pukdee, and Stefano Rini. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.” IEEE Information Theory Workshop. IEEE, 2022. https://doi.org/10.1109/ITW54588.2022.9965870. ieee: M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, “Sharp asymptotics on the compression of two-layer neural networks,” IEEE Information Theory Workshop. IEEE, pp. 588–593, 2022. ista: Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. 2022. Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop., 588–593. mla: Amani, Mohammad Hossein, et al. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.” IEEE Information Theory Workshop, IEEE, 2022, pp. 588–93, doi:10.1109/ITW54588.2022.9965870. short: M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information Theory Workshop (2022) 588–593. conference: end_date: 2022-11-09 location: Mumbai, India name: 'ITW: Information Theory Workshop' start_date: 2022-11-01 date_created: 2023-02-10T13:47:56Z date_published: 2022-11-16T00:00:00Z date_updated: 2025-09-10T09:53:31Z day: '16' department: - _id: MaMo doi: 10.1109/ITW54588.2022.9965870 external_id: arxiv: - '2205.08199' isi: - '000904341100099' fulldoi: https://doi.org/10.1109/ITW54588.2022.9965870 isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2205.08199' month: '11' oa: 1 oa_version: Preprint page: 588-593 publication: IEEE Information Theory Workshop publication_identifier: isbn: - '9781665483414' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Sharp asymptotics on the compression of two-layer neural networks type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2022' ... --- _id: '12540' abstract: - lang: eng text: We consider the problem of signal estimation in generalized linear models defined via rotationally invariant design matrices. Since these matrices can have an arbitrary spectral distribution, this model is well suited for capturing complex correlation structures which often arise in applications. We propose a novel family of approximate message passing (AMP) algorithms for signal estimation, and rigorously characterize their performance in the high-dimensional limit via a state evolution recursion. Our rotationally invariant AMP has complexity of the same order as the existing AMP derived under the restrictive assumption of a Gaussian design; our algorithm also recovers this existing AMP as a special case. Numerical results showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal in some settings), but obtained with a much lower complexity, as the proposed algorithm does not require a computationally expensive singular value decomposition. acknowledgement: The authors would like to thank the anonymous reviewers for their helpful comments. KK and MM were partially supported by the 2019 Lopez-Loreta Prize. article_number: '22' article_processing_charge: No author: - first_name: Ramji full_name: Venkataramanan, Ramji last_name: Venkataramanan - first_name: Kevin full_name: Kögler, Kevin id: 94ec913c-dc85-11ea-9058-e5051ab2428b last_name: Kögler - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Venkataramanan R, Kögler K, Mondelli M. Estimation in rotationally invariant generalized linear models via approximate message passing. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022.' apa: 'Venkataramanan, R., Kögler, K., & Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162). Baltimore, MD, United States: ML Research Press.' chicago: Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” In Proceedings of the 39th International Conference on Machine Learning, Vol. 162. ML Research Press, 2022. ieee: R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally invariant generalized linear models via approximate message passing,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162. ista: 'Venkataramanan R, Kögler K, Mondelli M. 2022. Estimation in rotationally invariant generalized linear models via approximate message passing. Proceedings of the 39th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 162, 22.' mla: Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, 22, ML Research Press, 2022. short: R. Venkataramanan, K. Kögler, M. Mondelli, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022. conference: end_date: 2022-07-23 location: Baltimore, MD, United States name: 'ICML: International Conference on Machine Learning' start_date: 2022-07-17 corr_author: '1' date_created: 2023-02-10T13:49:04Z date_published: 2022-01-01T00:00:00Z date_updated: 2025-04-15T07:50:16Z ddc: - '000' department: - _id: MaMo file: - access_level: open_access checksum: 67436eb0a660789514cdf9db79e84683 content_type: application/pdf creator: dernst date_created: 2023-02-13T10:53:11Z date_updated: 2023-02-13T10:53:11Z file_id: '12547' file_name: 2022_PMLR_Venkataramanan.pdf file_size: 2341343 relation: main_file success: 1 file_date_updated: 2023-02-13T10:53:11Z has_accepted_license: '1' intvolume: ' 162' language: - iso: eng oa: 1 oa_version: Published Version project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: Proceedings of the 39th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: Estimation in rotationally invariant generalized linear models via approximate message passing type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 162 year: '2022' ... --- _id: '12568' abstract: - lang: eng text: We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models. article_processing_charge: No arxiv: 1 author: - first_name: Tobias full_name: Meggendorfer, Tobias id: b21b0c15-30a2-11eb-80dc-f13ca25802e1 last_name: Meggendorfer orcid: 0000-0002-1712-2165 citation: ama: 'Meggendorfer T. Risk-aware stochastic shortest path. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Vol 36. Association for the Advancement of Artificial Intelligence; 2022:9858-9867. doi:10.1609/aaai.v36i9.21222' apa: 'Meggendorfer, T. (2022). Risk-aware stochastic shortest path. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (Vol. 36, pp. 9858–9867). Virtual: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i9.21222' chicago: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” In Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, 36:9858–67. Association for the Advancement of Artificial Intelligence, 2022. https://doi.org/10.1609/aaai.v36i9.21222. ieee: T. Meggendorfer, “Risk-aware stochastic shortest path,” in Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Virtual, 2022, vol. 36, no. 9, pp. 9858–9867. ista: Meggendorfer T. 2022. Risk-aware stochastic shortest path. Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Conference on Artificial Intelligence vol. 36, 9858–9867. mla: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, vol. 36, no. 9, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–67, doi:10.1609/aaai.v36i9.21222. short: T. Meggendorfer, in:, Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–9867. conference: end_date: 2022-03-01 location: Virtual name: Conference on Artificial Intelligence start_date: 2022-02-22 corr_author: '1' date_created: 2023-02-19T23:00:56Z date_published: 2022-06-28T00:00:00Z date_updated: 2024-10-09T21:04:32Z day: '28' department: - _id: KrCh doi: 10.1609/aaai.v36i9.21222 external_id: arxiv: - '2203.01640' fulldoi: https://doi.org/10.1609/aaai.v36i9.21222 intvolume: ' 36' issue: '9' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2203.01640' month: '06' oa: 1 oa_version: Preprint page: 9858-9867 publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 publication_identifier: eissn: - 2374-3468 isbn: - '1577358767' publication_status: published publisher: Association for the Advancement of Artificial Intelligence quality_controlled: '1' scopus_import: '1' status: public title: Risk-aware stochastic shortest path type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 36 year: '2022' ... --- _id: '12670' abstract: - lang: eng text: DNA methylation plays essential homeostatic functions in eukaryotic genomes. In animals, DNA methylation is also developmentally regulated and, in turn, regulates development. In the past two decades, huge research effort has endorsed the understanding that DNA methylation plays a similar role in plant development, especially during sexual reproduction. The power of whole-genome sequencing and cell isolation techniques, as well as bioinformatics tools, have enabled recent studies to reveal dynamic changes in DNA methylation during germline development. Furthermore, the combination of these technological advances with genetics, developmental biology and cell biology tools has revealed functional methylation reprogramming events that control gene and transposon activities in flowering plant germlines. In this review, we discuss the major advances in our knowledge of DNA methylation dynamics during male and female germline development in flowering plants. article_processing_charge: No article_type: review author: - first_name: Shengbo full_name: He, Shengbo last_name: He - first_name: Xiaoqi full_name: Feng, Xiaoqi id: e0164712-22ee-11ed-b12a-d80fcdf35958 last_name: Feng orcid: 0000-0002-4008-1234 citation: ama: He S, Feng X. DNA methylation dynamics during germline development. Journal of Integrative Plant Biology. 2022;64(12):2240-2251. doi:10.1111/jipb.13422 apa: He, S., & Feng, X. (2022). DNA methylation dynamics during germline development. Journal of Integrative Plant Biology. Wiley. https://doi.org/10.1111/jipb.13422 chicago: He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.” Journal of Integrative Plant Biology. Wiley, 2022. https://doi.org/10.1111/jipb.13422. ieee: S. He and X. Feng, “DNA methylation dynamics during germline development,” Journal of Integrative Plant Biology, vol. 64, no. 12. Wiley, pp. 2240–2251, 2022. ista: He S, Feng X. 2022. DNA methylation dynamics during germline development. Journal of Integrative Plant Biology. 64(12), 2240–2251. mla: He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.” Journal of Integrative Plant Biology, vol. 64, no. 12, Wiley, 2022, pp. 2240–51, doi:10.1111/jipb.13422. short: S. He, X. Feng, Journal of Integrative Plant Biology 64 (2022) 2240–2251. date_created: 2023-02-23T09:15:57Z date_published: 2022-12-07T00:00:00Z date_updated: 2024-10-14T12:03:14Z day: '07' department: - _id: XiFe doi: 10.1111/jipb.13422 extern: '1' external_id: pmid: - '36478632' fulldoi: https://doi.org/10.1111/jipb.13422 intvolume: ' 64' issue: '12' keyword: - Plant Science - General Biochemistry - Genetics and Molecular Biology - Biochemistry language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1111/jipb.13422 month: '12' oa: 1 oa_version: Published Version page: 2240-2251 pmid: 1 publication: Journal of Integrative Plant Biology publication_identifier: eissn: - 1744-7909 issn: - 1672-9072 publication_status: published publisher: Wiley quality_controlled: '1' scopus_import: '1' status: public title: DNA methylation dynamics during germline development type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 64 year: '2022' ... --- _id: '12671' abstract: - lang: eng text: Sperm chromatin is typically transformed by protamines into a compact and transcriptionally inactive state1,2. Sperm cells of flowering plants lack protamines, yet they have small, transcriptionally active nuclei with chromatin condensed through an unknown mechanism3,4. Here we show that a histone variant, H2B.8, mediates sperm chromatin and nuclear condensation in Arabidopsis thaliana. Loss of H2B.8 causes enlarged sperm nuclei with dispersed chromatin, whereas ectopic expression in somatic cells produces smaller nuclei with aggregated chromatin. This result demonstrates that H2B.8 is sufficient for chromatin condensation. H2B.8 aggregates transcriptionally inactive AT-rich chromatin into phase-separated condensates, which facilitates nuclear compaction without reducing transcription. Reciprocal crosses show that mutation of h2b.8 reduces male transmission, which suggests that H2B.8-mediated sperm compaction is important for fertility. Altogether, our results reveal a new mechanism of nuclear compaction through global aggregation of unexpressed chromatin. We propose that H2B.8 is an evolutionary innovation of flowering plants that achieves nuclear condensation compatible with active transcription. article_processing_charge: No article_type: original author: - first_name: Toby full_name: Buttress, Toby last_name: Buttress - first_name: Shengbo full_name: He, Shengbo last_name: He - first_name: Liang full_name: Wang, Liang last_name: Wang - first_name: Shaoli full_name: Zhou, Shaoli last_name: Zhou - first_name: Gerhard full_name: Saalbach, Gerhard last_name: Saalbach - first_name: Martin full_name: Vickers, Martin last_name: Vickers - first_name: Guohong full_name: Li, Guohong last_name: Li - first_name: Pilong full_name: Li, Pilong last_name: Li - first_name: Xiaoqi full_name: Feng, Xiaoqi id: e0164712-22ee-11ed-b12a-d80fcdf35958 last_name: Feng orcid: 0000-0002-4008-1234 citation: ama: Buttress T, He S, Wang L, et al. Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. Nature. 2022;611(7936):614-622. doi:10.1038/s41586-022-05386-6 apa: Buttress, T., He, S., Wang, L., Zhou, S., Saalbach, G., Vickers, M., … Feng, X. (2022). Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. Nature. Springer Nature. https://doi.org/10.1038/s41586-022-05386-6 chicago: Buttress, Toby, Shengbo He, Liang Wang, Shaoli Zhou, Gerhard Saalbach, Martin Vickers, Guohong Li, Pilong Li, and Xiaoqi Feng. “Histone H2B.8 Compacts Flowering Plant Sperm through Chromatin Phase Separation.” Nature. Springer Nature, 2022. https://doi.org/10.1038/s41586-022-05386-6. ieee: T. Buttress et al., “Histone H2B.8 compacts flowering plant sperm through chromatin phase separation,” Nature, vol. 611, no. 7936. Springer Nature, pp. 614–622, 2022. ista: Buttress T, He S, Wang L, Zhou S, Saalbach G, Vickers M, Li G, Li P, Feng X. 2022. Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. Nature. 611(7936), 614–622. mla: Buttress, Toby, et al. “Histone H2B.8 Compacts Flowering Plant Sperm through Chromatin Phase Separation.” Nature, vol. 611, no. 7936, Springer Nature, 2022, pp. 614–22, doi:10.1038/s41586-022-05386-6. short: T. Buttress, S. He, L. Wang, S. Zhou, G. Saalbach, M. Vickers, G. Li, P. Li, X. Feng, Nature 611 (2022) 614–622. date_created: 2023-02-23T09:17:05Z date_published: 2022-11-17T00:00:00Z date_updated: 2024-10-14T12:03:36Z day: '17' department: - _id: XiFe doi: 10.1038/s41586-022-05386-6 extern: '1' external_id: pmid: - '36323776' fulldoi: https://doi.org/10.1038/s41586-022-05386-6 intvolume: ' 611' issue: '7936' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1038/s41586-022-05386-6 month: '11' oa: 1 oa_version: Published Version page: 614-622 pmid: 1 publication: Nature publication_identifier: eissn: - 1476-4687 issn: - 0028-0836 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Histone H2B.8 compacts flowering plant sperm through chromatin phase separation type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 611 year: '2022' ... --- _id: '12677' abstract: - lang: eng text: "In modern sample-driven Prophet Inequality, an adversary chooses a sequence of n items with values v1,v2,…,vn to be presented to a decision maker (DM). The process follows in two phases. In the first phase (sampling phase), some items, possibly selected at random, are revealed to the DM, but she can never accept them. In the second phase, the DM is presented with the other items in a random order and online fashion. For each item, she must make an irrevocable decision to either accept the item and stop the process or reject the item forever and proceed to the next item. The goal of the DM is to maximize the expected value as compared to a Prophet (or offline algorithm) that has access to all information. In this setting, the sampling phase has no cost and is not part of the optimization process. However, in many scenarios, the samples are obtained as part of the decision-making process.\r\nWe model this aspect as a two-phase Prophet Inequality where an adversary chooses a sequence of 2n items with values v1,v2,…,v2n and the items are randomly ordered. Finally, there are two phases of the Prophet Inequality problem with the first n-items and the rest of the items, respectively. We show that some basic algorithms achieve a ratio of at most 0.450. We present an algorithm that achieves a ratio of at least 0.495. Finally, we show that for every algorithm the ratio it can achieve is at most 0.502. Hence our algorithm is near-optimal." acknowledgement: This research was partially supported by the ERC CoG 863818 (ForM-SMArt) grant. article_number: '2209.14368' article_processing_charge: No arxiv: 1 author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Mona full_name: Mohammadi, Mona id: 4363614d-b686-11ed-a7d5-ac9e4a24bc2e last_name: Mohammadi - first_name: Raimundo J full_name: Saona Urmeneta, Raimundo J id: BD1DF4C4-D767-11E9-B658-BC13E6697425 last_name: Saona Urmeneta orcid: 0000-0001-5103-038X citation: ama: Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with near-optimal bounds. arXiv. doi:10.48550/ARXIV.2209.14368 apa: Chatterjee, K., Mohammadi, M., & Saona Urmeneta, R. J. (n.d.). Repeated prophet inequality with near-optimal bounds. arXiv. https://doi.org/10.48550/ARXIV.2209.14368 chicago: Chatterjee, Krishnendu, Mona Mohammadi, and Raimundo J Saona Urmeneta. “Repeated Prophet Inequality with Near-Optimal Bounds.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2209.14368. ieee: K. Chatterjee, M. Mohammadi, and R. J. Saona Urmeneta, “Repeated prophet inequality with near-optimal bounds,” arXiv. . ista: Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with near-optimal bounds. arXiv, 2209.14368. mla: Chatterjee, Krishnendu, et al. “Repeated Prophet Inequality with Near-Optimal Bounds.” ArXiv, 2209.14368, doi:10.48550/ARXIV.2209.14368. short: K. Chatterjee, M. Mohammadi, R.J. Saona Urmeneta, ArXiv (n.d.). corr_author: '1' date_created: 2023-02-24T12:21:40Z date_published: 2022-09-28T00:00:00Z date_updated: 2025-04-14T07:52:48Z day: '28' department: - _id: GradSch - _id: KrCh doi: 10.48550/ARXIV.2209.14368 ec_funded: 1 external_id: arxiv: - '2209.14368' fulldoi: https://doi.org/10.48550/ARXIV.2209.14368 language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2209.14368' month: '09' oa: 1 oa_version: Preprint project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: arXiv publication_status: submitted status: public title: Repeated prophet inequality with near-optimal bounds type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- OA_place: repository _id: '12750' abstract: - lang: eng text: Quantum kinetically constrained models have recently attracted significant attention due to their anomalous dynamics and thermalization. In this work, we introduce a hitherto unexplored family of kinetically constrained models featuring a conserved particle number and strong inversion-symmetry breaking due to facilitated hopping. We demonstrate that these models provide a generic example of so-called quantum Hilbert space fragmentation, that is manifested in disconnected sectors in the Hilbert space that are not apparent in the computational basis. Quantum Hilbert space fragmentation leads to an exponential in system size number of eigenstates with exactly zero entanglement entropy across several bipartite cuts. These eigenstates can be probed dynamically using quenches from simple initial product states. In addition, we study the particle spreading under unitary dynamics launched from the domain wall state, and find faster than diffusive dynamics at high particle densities, that crosses over into logarithmically slow relaxation at smaller densities. Using a classically simulable cellular automaton, we reproduce the logarithmic dynamics observed in the quantum case. Our work suggests that particle conserving constrained models with inversion symmetry breaking realize so far unexplored universality classes of dynamics and invite their further theoretical and experimental studies. article_number: '2210.15607' article_processing_charge: No arxiv: 1 author: - first_name: Pietro full_name: Brighi, Pietro id: 4115AF5C-F248-11E8-B48F-1D18A9856A87 last_name: Brighi orcid: 0000-0002-7969-2729 - first_name: Marko full_name: Ljubotina, Marko id: F75EE9BE-5C90-11EA-905D-16643DDC885E last_name: Ljubotina orcid: 0000-0003-0038-7068 - first_name: Maksym full_name: Serbyn, Maksym id: 47809E7E-F248-11E8-B48F-1D18A9856A87 last_name: Serbyn orcid: 0000-0002-2399-5827 citation: ama: Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. arXiv. doi:10.48550/arXiv.2210.15607 apa: Brighi, P., Ljubotina, M., & Serbyn, M. (n.d.). Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. arXiv. https://doi.org/10.48550/arXiv.2210.15607 chicago: Brighi, Pietro, Marko Ljubotina, and Maksym Serbyn. “Hilbert Space Fragmentation and Slow Dynamics in Particle-Conserving Quantum East Models.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2210.15607. ieee: P. Brighi, M. Ljubotina, and M. Serbyn, “Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models,” arXiv. . ista: Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. arXiv, 2210.15607. mla: Brighi, Pietro, et al. “Hilbert Space Fragmentation and Slow Dynamics in Particle-Conserving Quantum East Models.” ArXiv, 2210.15607, doi:10.48550/arXiv.2210.15607. short: P. Brighi, M. Ljubotina, M. Serbyn, ArXiv (n.d.). corr_author: '1' date_created: 2023-03-23T14:33:13Z date_published: 2022-11-07T00:00:00Z date_updated: 2026-04-07T13:26:31Z day: '07' department: - _id: GradSch - _id: MaSe doi: 10.48550/arXiv.2210.15607 external_id: arxiv: - '2210.15607' fulldoi: https://doi.org/10.48550/arXiv.2210.15607 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2210.15607 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: draft related_material: record: - id: '14334' relation: later_version status: public - id: '12732' relation: dissertation_contains status: public status: public title: Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '12776' abstract: - lang: eng text: An improved asymptotic formula is established for the number of rational points of bounded height on the split smooth del Pezzo surface of degree 5. The proof uses the five conic bundle structures on the surface. acknowledgement: This work was begun while the author was participating in the programme on "Diophantine equations" at the Hausdorff Research Institute for Mathematics in Bonn in 2009. The hospitality and financial support of the institute is gratefully acknowledged. The idea of using conic bundles to study the split del Pezzo surface of degree 5 was explained to the author by Professor Salberger. The author is very grateful to him for his input into this project and also to Shuntaro Yamagishi for many useful comments on an earlier version of this manuscript. While working on this paper the author was supported by FWF grant P32428-N35. article_processing_charge: No article_type: original author: - first_name: Timothy D full_name: Browning, Timothy D id: 35827D50-F248-11E8-B48F-1D18A9856A87 last_name: Browning orcid: 0000-0002-8314-0177 citation: ama: Browning TD. Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. New York Journal of Mathematics. 2022;28:1193-1229. apa: Browning, T. D. (2022). Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. New York Journal of Mathematics. State University of New York. chicago: Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del Pezzo Surface of Degree 5.” New York Journal of Mathematics. State University of New York, 2022. ieee: T. D. Browning, “Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5,” New York Journal of Mathematics, vol. 28. State University of New York, pp. 1193–1229, 2022. ista: Browning TD. 2022. Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. New York Journal of Mathematics. 28, 1193–1229. mla: Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del Pezzo Surface of Degree 5.” New York Journal of Mathematics, vol. 28, State University of New York, 2022, pp. 1193–229. short: T.D. Browning, New York Journal of Mathematics 28 (2022) 1193–1229. corr_author: '1' date_created: 2023-03-28T09:21:09Z date_published: 2022-08-24T00:00:00Z date_updated: 2025-04-15T07:39:01Z day: '24' ddc: - '510' department: - _id: TiBr file: - access_level: open_access checksum: c01e8291794a1bdb7416aa103cb68ef8 content_type: application/pdf creator: dernst date_created: 2023-03-30T07:09:35Z date_updated: 2023-03-30T07:09:35Z file_id: '12778' file_name: 2022_NYJM_Browning.pdf file_size: 897267 relation: main_file success: 1 file_date_updated: 2023-03-30T07:09:35Z has_accepted_license: '1' intvolume: ' 28' language: - iso: eng month: '08' oa: 1 oa_version: Published Version page: 1193 - 1229 project: - _id: 26AEDAB2-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P32428 name: New frontiers of the Manin conjecture publication: New York Journal of Mathematics publication_identifier: issn: - 1076-9803 publication_status: published publisher: State University of New York quality_controlled: '1' status: public title: Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5 tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 28 year: '2022' ... --- _id: '12860' abstract: - lang: eng text: 'Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) to understand, quantify and control this phenomenon. While bounding general memorization can have detrimental effects on the performance of a trained model, bounding RM does not prevent effective learning. The difference is most pronounced when the data distribution is long-tailed, with many queries having only few training examples: Impeding general memorization prevents effective learning, while impeding only relational memorization still allows learning general properties of the underlying concepts. We formalize the notion of Relational Privacy (RP) and, inspired by Differential Privacy (DP), we provide a possible definition of Differential Relational Privacy (DrP). These notions can be used to describe and compute bounds on the amount of RM in a trained model. We illustrate Relational Privacy concepts in experiments with large-scale models for Question Answering.' article_number: '2203.16701' article_processing_charge: No arxiv: 1 author: - first_name: Simone full_name: Bombari, Simone id: ca726dda-de17-11ea-bc14-f9da834f63aa last_name: Bombari - first_name: Alessandro full_name: Achille, Alessandro last_name: Achille - first_name: Zijian full_name: Wang, Zijian last_name: Wang - first_name: Yu-Xiang full_name: Wang, Yu-Xiang last_name: Wang - first_name: Yusheng full_name: Xie, Yusheng last_name: Xie - first_name: Kunwar Yashraj full_name: Singh, Kunwar Yashraj last_name: Singh - first_name: Srikar full_name: Appalaraju, Srikar last_name: Appalaraju - first_name: Vijay full_name: Mahadevan, Vijay last_name: Mahadevan - first_name: Stefano full_name: Soatto, Stefano last_name: Soatto citation: ama: Bombari S, Achille A, Wang Z, et al. Towards differential relational privacy and its use in question answering. arXiv. doi:10.48550/arXiv.2203.16701 apa: Bombari, S., Achille, A., Wang, Z., Wang, Y.-X., Xie, Y., Singh, K. Y., … Soatto, S. (n.d.). Towards differential relational privacy and its use in question answering. arXiv. https://doi.org/10.48550/arXiv.2203.16701 chicago: Bombari, Simone, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, and Stefano Soatto. “Towards Differential Relational Privacy and Its Use in Question Answering.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2203.16701. ieee: S. Bombari et al., “Towards differential relational privacy and its use in question answering,” arXiv. . ista: Bombari S, Achille A, Wang Z, Wang Y-X, Xie Y, Singh KY, Appalaraju S, Mahadevan V, Soatto S. Towards differential relational privacy and its use in question answering. arXiv, 2203.16701. mla: Bombari, Simone, et al. “Towards Differential Relational Privacy and Its Use in Question Answering.” ArXiv, 2203.16701, doi:10.48550/arXiv.2203.16701. short: S. Bombari, A. Achille, Z. Wang, Y.-X. Wang, Y. Xie, K.Y. Singh, S. Appalaraju, V. Mahadevan, S. Soatto, ArXiv (n.d.). date_created: 2023-04-23T16:11:48Z date_published: 2022-03-30T00:00:00Z date_updated: 2023-04-25T07:34:49Z day: '30' department: - _id: GradSch - _id: MaMo doi: 10.48550/arXiv.2203.16701 external_id: arxiv: - '2203.16701' fulldoi: https://doi.org/10.48550/arXiv.2203.16701 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2203.16701 month: '03' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Towards differential relational privacy and its use in question answering type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '13064' abstract: - lang: eng text: Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies. article_processing_charge: No author: - first_name: Etienne full_name: Orliac, Etienne last_name: Orliac - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Sven full_name: Ojavee, Sven last_name: Ojavee - first_name: Kristi full_name: Läll, Kristi last_name: Läll - first_name: Reedik full_name: Mägi, Reedik last_name: Mägi - first_name: Peter full_name: Visscher, Peter last_name: Visscher - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:10.5061/DRYAD.GTHT76HMZ apa: Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., & Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. https://doi.org/10.5061/DRYAD.GTHT76HMZ chicago: Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi, Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. https://doi.org/10.5061/DRYAD.GTHT76HMZ. ieee: E. Orliac et al., “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022. ista: Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, 10.5061/DRYAD.GTHT76HMZ. mla: Orliac, Etienne, et al. Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data. Dryad, 2022, doi:10.5061/DRYAD.GTHT76HMZ. short: E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022). corr_author: '1' date_created: 2023-05-23T16:28:13Z date_published: 2022-09-02T00:00:00Z date_updated: 2025-06-12T06:22:36Z day: '02' ddc: - '570' department: - _id: MaRo doi: 10.5061/DRYAD.GTHT76HMZ fulldoi: https://doi.org/10.5061/DRYAD.GTHT76HMZ main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.gtht76hmz month: '09' oa: 1 oa_version: Published Version publisher: Dryad related_material: record: - id: '11733' relation: used_in_publication status: public status: public title: Improving genome-wide association discovery and genomic prediction accuracy in biobank data tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '13066' abstract: - lang: eng text: Chromosomal inversions have been shown to play a major role in local adaptation by suppressing recombination between alternative arrangements and maintaining beneficial allele combinations. However, so far, their importance relative to the remaining genome remains largely unknown. Understanding the genetic architecture of adaptation requires better estimates of how loci of different effect sizes contribute to phenotypic variation. Here, we used three Swedish islands where the marine snail Littorina saxatilis has repeatedly evolved into two distinct ecotypes along a habitat transition. We estimated the contribution of inversion polymorphisms to phenotypic divergence while controlling for polygenic effects in the remaining genome using a quantitative genetics framework. We confirmed the importance of inversions but showed that contributions of loci outside inversions are of similar magnitude, with variable proportions dependent on the trait and the population. Some inversions showed consistent effects across all sites, whereas others exhibited site-specific effects, indicating that the genomic basis for replicated phenotypic divergence is only partly shared. The contributions of sexual dimorphism as well as environmental factors to phenotypic variation were significant but minor compared to inversions and polygenic background. Overall, this integrated approach provides insight into the multiple mechanisms contributing to parallel phenotypic divergence. article_processing_charge: No author: - first_name: Eva full_name: Koch, Eva last_name: Koch - first_name: Mark full_name: Ravinet, Mark last_name: Ravinet - first_name: Anja M full_name: Westram, Anja M id: 3C147470-F248-11E8-B48F-1D18A9856A87 last_name: Westram orcid: 0000-0003-1050-4969 - first_name: Kerstin full_name: Jonannesson, Kerstin last_name: Jonannesson - first_name: Roger full_name: Butlin, Roger last_name: Butlin citation: ama: 'Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. 2022. doi:10.5061/DRYAD.M905QFV4B' apa: 'Koch, E., Ravinet, M., Westram, A. M., Jonannesson, K., & Butlin, R. (2022). Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. Dryad. https://doi.org/10.5061/DRYAD.M905QFV4B' chicago: 'Koch, Eva, Mark Ravinet, Anja M Westram, Kerstin Jonannesson, and Roger Butlin. “Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution.” Dryad, 2022. https://doi.org/10.5061/DRYAD.M905QFV4B.' ieee: 'E. Koch, M. Ravinet, A. M. Westram, K. Jonannesson, and R. Butlin, “Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution.” Dryad, 2022.' ista: 'Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. 2022. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution, Dryad, 10.5061/DRYAD.M905QFV4B.' mla: 'Koch, Eva, et al. Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution. Dryad, 2022, doi:10.5061/DRYAD.M905QFV4B.' short: E. Koch, M. Ravinet, A.M. Westram, K. Jonannesson, R. Butlin, (2022). date_created: 2023-05-23T16:33:12Z date_published: 2022-07-28T00:00:00Z date_updated: 2023-08-04T09:42:10Z day: '28' ddc: - '570' department: - _id: NiBa doi: 10.5061/DRYAD.M905QFV4B fulldoi: https://doi.org/10.5061/DRYAD.M905QFV4B main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.m905qfv4b month: '07' oa: 1 oa_version: Published Version publisher: Dryad related_material: record: - id: '12247' relation: used_in_publication status: public status: public title: 'Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution' tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '13076' abstract: - lang: eng text: "The source code for replicating experiments presented in the paper.\r\n\r\nThe implementation of the designed priority schedulers can be found in Galois-2.2.1/include/Galois/WorkList/:\r\nStealingMultiQueue.h is the StealingMultiQueue.\r\nMQOptimized/ contains MQ Optimized variants.\r\n\r\nWe provide images that contain all the dependencies and datasets. Images can be pulled from npostnikova/mq-based-schedulers repository, or downloaded from Zenodo. See readme for more detail." article_processing_charge: No author: - first_name: Anastasiia full_name: Postnikova, Anastasiia last_name: Postnikova - first_name: Nikita full_name: Koval, Nikita id: 2F4DB10C-F248-11E8-B48F-1D18A9856A87 last_name: Koval - first_name: Giorgi full_name: Nadiradze, Giorgi id: 3279A00C-F248-11E8-B48F-1D18A9856A87 last_name: Nadiradze orcid: 0000-0001-5634-0731 - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:10.5281/ZENODO.5733408 apa: Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408 chicago: Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. https://doi.org/10.5281/ZENODO.5733408. ieee: A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022. ista: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers, Zenodo, 10.5281/ZENODO.5733408. mla: Postnikova, Anastasiia, et al. Multi-Queues Can Be State-of-the-Art Priority Schedulers. Zenodo, 2022, doi:10.5281/ZENODO.5733408. short: A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022). corr_author: '1' date_created: 2023-05-23T17:05:40Z date_published: 2022-01-03T00:00:00Z date_updated: 2025-04-14T13:51:59Z day: '03' ddc: - '510' department: - _id: DaAl doi: 10.5281/ZENODO.5733408 fulldoi: https://doi.org/10.5281/ZENODO.5733408 main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.5813846 month: '01' oa: 1 oa_version: Published Version publisher: Zenodo related_material: link: - relation: software url: https://github.com/npostnikova/mq-based-schedulers/tree/v1.1 record: - id: '11180' relation: used_in_publication status: public status: public title: Multi-queues can be state-of-the-art priority schedulers type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '13239' abstract: - lang: eng text: Brains are thought to engage in predictive learning - learning to predict upcoming stimuli - to construct an internal model of their environment. This is especially notable for spatial navigation, as first described by Tolman’s latent learning tasks. However, predictive learning has also been observed in sensory cortex, in settings unrelated to spatial navigation. Apart from normative frameworks such as active inference or efficient coding, what could be the utility of learning to predict the patterns of occurrence of correlated stimuli? Here we show that prediction, and thereby the construction of an internal model of sequential stimuli, can bootstrap the learning process of a working memory task in a recurrent neural network. We implemented predictive learning alongside working memory match-tasks, and networks emerged to solve the prediction task first by encoding information across time to predict upcoming stimuli, and then eavesdropped on this solution to solve the matching task. Eavesdropping was most beneficial when neural resources were limited. Hence, predictive learning acts as a general neural mechanism to learn to store sensory information that can later be essential for working memory tasks. acknowledgement: "The authors would like to thank members of the Vogels lab and Manohar lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X and Leverhulme Grant RPG-2018-310." article_processing_charge: No author: - first_name: Thijs L. full_name: Van Der Plas, Thijs L. last_name: Van Der Plas - first_name: Tim P full_name: Vogels, Tim P id: CB6FF8D2-008F-11EA-8E08-2637E6697425 last_name: Vogels orcid: 0000-0003-3295-6181 - first_name: Sanjay G. full_name: Manohar, Sanjay G. last_name: Manohar citation: ama: 'Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural networks to learn complex working memory tasks. In: Proceedings of Machine Learning Research. Vol 199. ML Research Press; 2022:518-531.' apa: Van Der Plas, T. L., Vogels, T. P., & Manohar, S. G. (2022). Predictive learning enables neural networks to learn complex working memory tasks. In Proceedings of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press. chicago: Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In Proceedings of Machine Learning Research, 199:518–31. ML Research Press, 2022. ieee: T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning enables neural networks to learn complex working memory tasks,” in Proceedings of Machine Learning Research, 2022, vol. 199, pp. 518–531. ista: Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables neural networks to learn complex working memory tasks. Proceedings of Machine Learning Research. vol. 199, 518–531. mla: Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” Proceedings of Machine Learning Research, vol. 199, ML Research Press, 2022, pp. 518–31. short: T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 518–531. date_created: 2023-07-16T22:01:12Z date_published: 2022-12-01T00:00:00Z date_updated: 2025-04-14T07:54:31Z day: '01' ddc: - '000' department: - _id: TiVo ec_funded: 1 file: - access_level: open_access checksum: 7530a93ef42e10b4db1e5e4b69796e93 content_type: application/pdf creator: dernst date_created: 2023-07-18T06:32:38Z date_updated: 2023-07-18T06:32:38Z file_id: '13243' file_name: 2022_PMLR_vanderPlas.pdf file_size: 585135 relation: main_file success: 1 file_date_updated: 2023-07-18T06:32:38Z has_accepted_license: '1' intvolume: ' 199' language: - iso: eng month: '12' oa: 1 oa_version: Published Version page: 518-531 project: - _id: 0aacfa84-070f-11eb-9043-d7eb2c709234 call_identifier: H2020 grant_number: '819603' name: Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning. publication: Proceedings of Machine Learning Research publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Predictive learning enables neural networks to learn complex working memory tasks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 199 year: '2022' ... --- _id: '13240' abstract: - lang: eng text: Ustilago maydis is a biotrophic phytopathogenic fungus that causes corn smut disease. As a well-established model system, U. maydis is genetically fully accessible with large omics datasets available and subject to various biological questions ranging from DNA-repair, RNA-transport, and protein secretion to disease biology. For many genetic approaches, tight control of transgene regulation is important. Here we established an optimised version of the Tetracycline-ON (TetON) system for U. maydis. We demonstrate the Tetracycline concentration-dependent expression of fluorescent protein transgenes and the system’s suitability for the induced expression of the toxic protein BCL2 Associated X-1 (Bax1). The Golden Gate compatible vector system contains a native minimal promoter from the mating factor a-1 encoding gene, mfa with ten copies of the tet-regulated operator (tetO) and a codon optimised Tet-repressor (tetR*) which is translationally fused to the native transcriptional corepressor Mql1 (UMAG_05501). The metabolism-independent transcriptional regulator system is functional both, in liquid culture as well as on solid media in the presence of the inducer and can become a useful tool for toxin-antitoxin studies, identification of antifungal proteins, and to study functions of toxic gene products in Ustilago maydis. acknowledgement: "The research leading to these results received funding from the European Research Council under the European Union’s Seventh Framework Programme ERC-2013-STG (grant agreement: 335691), the Austrian Science Fund (I 3033-B22), the Austrian Academy of Sciences, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy EXC-2070-390732324 (PhenoRob) and DFG grant (DJ 64/5-1).\r\nWe would like to thank the GMI/IMBA/IMP core facilities for their excellent technical support. We would like to acknowledge Dr. Sinéad A. O’Sullivan from DZNE, University of Bonn for providing anti-GFP antibodies. The authors are thankful to the Excellence University of Bonn for providing infrastructure and instrumentation facilities at the INRES-Plant Pathology department." article_number: '1029114' article_processing_charge: Yes article_type: original author: - first_name: Kishor D. full_name: Ingole, Kishor D. last_name: Ingole - first_name: Nithya full_name: Nagarajan, Nithya last_name: Nagarajan - first_name: Simon full_name: Uhse, Simon last_name: Uhse - first_name: Caterina full_name: Giannini, Caterina id: e3fdddd5-f6e0-11ea-865d-ca99ee6367f4 last_name: Giannini - first_name: Armin full_name: Djamei, Armin last_name: Djamei citation: ama: Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers in Fungal Biology. 2022;3. doi:10.3389/ffunb.2022.1029114 apa: Ingole, K. D., Nagarajan, N., Uhse, S., Giannini, C., & Djamei, A. (2022). Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers in Fungal Biology. Frontiers Media. https://doi.org/10.3389/ffunb.2022.1029114 chicago: Ingole, Kishor D., Nithya Nagarajan, Simon Uhse, Caterina Giannini, and Armin Djamei. “Tetracycline-Controlled (TetON) Gene Expression System for the Smut Fungus Ustilago Maydis.” Frontiers in Fungal Biology. Frontiers Media, 2022. https://doi.org/10.3389/ffunb.2022.1029114. ieee: K. D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, and A. Djamei, “Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis,” Frontiers in Fungal Biology, vol. 3. Frontiers Media, 2022. ista: Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. 2022. Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers in Fungal Biology. 3, 1029114. mla: Ingole, Kishor D., et al. “Tetracycline-Controlled (TetON) Gene Expression System for the Smut Fungus Ustilago Maydis.” Frontiers in Fungal Biology, vol. 3, 1029114, Frontiers Media, 2022, doi:10.3389/ffunb.2022.1029114. short: K.D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, A. Djamei, Frontiers in Fungal Biology 3 (2022). date_created: 2023-07-16T22:01:12Z date_published: 2022-10-19T00:00:00Z date_updated: 2024-03-06T14:01:57Z day: '19' ddc: - '579' department: - _id: JiFr doi: 10.3389/ffunb.2022.1029114 file: - access_level: open_access checksum: 2254e0119c0749d6f7237084fefcece6 content_type: application/pdf creator: dernst date_created: 2023-07-17T11:46:34Z date_updated: 2023-07-17T11:46:34Z file_id: '13242' file_name: 2023_FrontiersFungalBio_Ingole.pdf file_size: 27966699 relation: main_file success: 1 file_date_updated: 2023-07-17T11:46:34Z fulldoi: https://doi.org/10.3389/ffunb.2022.1029114 has_accepted_license: '1' intvolume: ' 3' language: - iso: eng month: '10' oa: 1 oa_version: Published Version publication: Frontiers in Fungal Biology publication_identifier: eissn: - 2673-6128 publication_status: published publisher: Frontiers Media quality_controlled: '1' scopus_import: '1' status: public title: Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 3 year: '2022' ... --- _id: '13241' abstract: - lang: eng text: Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. Many approaches for training fair models from data have been developed and an implicit assumption about such algorithms is that they are able to recover a fair model, despite potential historical biases in the data. In this work we show a number of impossibility results that indicate that there is no learning algorithm that can recover a fair model when a proportion of the dataset is subject to arbitrary manipulations. Specifically, we prove that there are situations in which an adversary can force any learner to return a biased classifier, with or without degrading accuracy, and that the strength of this bias increases for learning problems with underrepresented protected groups in the data. Our results emphasize on the importance of studying further data corruption models of various strength and of establishing stricter data collection practices for fairness-aware learning. acknowledgement: "This paper is a shortened, workshop version of Konstantinov and Lampert (2021),\r\nhttps://arxiv.org/abs/2102.06004. For further results, including an analysis of algorithms achieving the lower bounds from this paper, we refer to the full version." article_processing_charge: No arxiv: 1 author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning from corrupted data. In: Proceedings of Machine Learning Research. Vol 171. ML Research Press; 2022:59-83.' apa: Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press. chicago: Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” In Proceedings of Machine Learning Research, 171:59–83. ML Research Press, 2022. ieee: N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83. ista: Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83. mla: Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” Proceedings of Machine Learning Research, vol. 171, ML Research Press, 2022, pp. 59–83. short: N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83. corr_author: '1' date_created: 2023-07-16T22:01:13Z date_published: 2022-12-01T00:00:00Z date_updated: 2024-10-09T21:05:54Z day: '01' department: - _id: ChLa external_id: arxiv: - '2102.06004' intvolume: ' 171' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2102.06004 month: '12' oa: 1 oa_version: Preprint page: 59-83 publication: Proceedings of Machine Learning Research publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '10802' relation: extended_version status: public scopus_import: '1' status: public title: On the impossibility of fairness-aware learning from corrupted data type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 171 year: '2022' ... --- _id: '14093' abstract: - lang: eng text: ' We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully increasing the batch size over the course of the algorithm’s execution, which leads to computing full gradients. In contrast, the proposed method, equipped with a stochastic average gradient (SAG) estimator, requires only one sample per iteration. Nevertheless, it guarantees fast convergence rates on par with more sophisticated variance reduction techniques. In applications we put special emphasis on problems with a large number of separable constraints. Such problems are prevalent among semidefinite programming (SDP) formulations arising in machine learning and theoretical computer science. We provide numerical experiments on matrix completion, unsupervised clustering, and sparsest-cut SDPs. ' alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Maria-Luiza full_name: Vladarean, Maria-Luiza last_name: Vladarean - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Volkan full_name: Cevher, Volkan last_name: Cevher - first_name: Alp full_name: Yurtsever, Alp last_name: Yurtsever citation: ama: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. Faster one-sample stochastic conditional gradient method for composite convex minimization. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. Vol 151. ML Research Press; 2022:8439-8457.' apa: 'Dresdner, G., Vladarean, M.-L., Rätsch, G., Locatello, F., Cevher, V., & Yurtsever, A. (2022). Faster one-sample stochastic conditional gradient method for composite convex minimization. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (Vol. 151, pp. 8439–8457). Virtual: ML Research Press.' chicago: Dresdner, Gideon, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, and Alp Yurtsever. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, 151:8439–57. ML Research Press, 2022. ieee: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever, “ Faster one-sample stochastic conditional gradient method for composite convex minimization,” in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, Virtual, 2022, vol. 151, pp. 8439–8457. ista: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. 2022. Faster one-sample stochastic conditional gradient method for composite convex minimization. Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 151, 8439–8457.' mla: Dresdner, Gideon, et al. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, vol. 151, ML Research Press, 2022, pp. 8439–57. short: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever, in:, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2022, pp. 8439–8457. conference: end_date: 2022-03-30 location: Virtual name: 'AISTATS: Conference on Artificial Intelligence and Statistics' start_date: 2022-03-28 date_created: 2023-08-21T09:27:43Z date_published: 2022-04-01T00:00:00Z date_updated: 2023-09-06T10:28:17Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2202.13212' intvolume: ' 151' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2202.13212 month: '04' oa: 1 oa_version: Preprint page: 8439-8457 publication: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics publication_identifier: issn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: ' Faster one-sample stochastic conditional gradient method for composite convex minimization' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 151 year: '2022' ... --- _id: '14106' abstract: - lang: eng text: "We show that deep networks trained to satisfy demographic parity often do so\r\nthrough a form of race or gender awareness, and that the more we force a network\r\nto be fair, the more accurately we can recover race or gender from the internal state\r\nof the network. Based on this observation, we investigate an alternative fairness\r\napproach: we add a second classification head to the network to explicitly predict\r\nthe protected attribute (such as race or gender) alongside the original task. After\r\ntraining the two-headed network, we enforce demographic parity by merging the\r\ntwo heads, creating a network with the same architecture as the original network.\r\nWe establish a close relationship between existing approaches and our approach\r\nby showing (1) that the decisions of a fair classifier are well-approximated by our\r\napproach, and (2) that an unfair and optimally accurate classifier can be recovered\r\nfrom a fair classifier and our second head predicting the protected attribute. We use\r\nour explicit formulation to argue that the existing fairness approaches, just as ours,\r\ndemonstrate disparate treatment and that they are likely to be unlawful in a wide\r\nrange of scenarios under US law." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Michael full_name: Lohaus, Michael last_name: Lohaus - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Krishnaram full_name: Kenthapadi, Krishnaram last_name: Kenthapadi - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Chris full_name: Russell, Chris last_name: Russell citation: ama: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads the same as one? Identifying disparate treatment in fair neural networks. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:16548-16562.' apa: 'Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., & Russell, C. (2022). Are two heads the same as one? Identifying disparate treatment in fair neural networks. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information Processing Systems Foundation.' chicago: Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” In 36th Conference on Neural Information Processing Systems, 35:16548–62. Neural Information Processing Systems Foundation, 2022. ieee: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are two heads the same as one? Identifying disparate treatment in fair neural networks,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 16548–16562. ista: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. 2022. Are two heads the same as one? Identifying disparate treatment in fair neural networks. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 16548–16562.' mla: Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 16548–62. short: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 16548–16562. conference: end_date: 2022-12-09 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-21T12:12:42Z date_published: 2022-12-15T00:00:00Z date_updated: 2024-10-14T12:27:01Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '2204.04440' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2204.04440 month: '12' oa: 1 oa_version: Preprint page: 16548-16562 publication: 36th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713871088' publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Are two heads the same as one? Identifying disparate treatment in fair neural networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14107' abstract: - lang: eng text: "Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information than what is contained in the instant retina or imaging sensor, (2) it is difficult to obtain enough well-annotated amodal labels for supervision. To this end, this paper develops a new framework of\r\nSelf-supervised amodal Video object segmentation (SaVos). Our method efficiently leverages the visual information of video temporal sequences to infer the amodal mask of objects. The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning paradigm that efficiently utilizes the visible object parts as the supervision to guide the training on videos. In addition to learning type prior to complete masks for known types, SaVos also learns the spatiotemporal prior, which is also useful for the amodal task and could generalize to unseen types. The proposed\r\nframework achieves the state-of-the-art performance on the synthetic amodal segmentation benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends itself well to being transferred to novel distributions using test-time adaptation, outperforming existing models even after the transfer to a new distribution." article_processing_charge: No arxiv: 1 author: - first_name: Jian full_name: Yao, Jian last_name: Yao - first_name: Yuxin full_name: Hong, Yuxin last_name: Hong - first_name: Chiyu full_name: Wang, Chiyu last_name: Wang - first_name: Tianjun full_name: Xiao, Tianjun last_name: Xiao - first_name: Tong full_name: He, Tong last_name: He - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: David full_name: Wipf, David last_name: Wipf - first_name: Yanwei full_name: Fu, Yanwei last_name: Fu - first_name: Zheng full_name: Zhang, Zheng last_name: Zhang citation: ama: 'Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: 36th Conference on Neural Information Processing Systems. ; 2022. doi:10.48550/arXiv.2210.12733' apa: Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022). Self-supervised amodal video object segmentation. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States. https://doi.org/10.48550/arXiv.2210.12733 chicago: Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.” In 36th Conference on Neural Information Processing Systems, 2022. https://doi.org/10.48550/arXiv.2210.12733. ieee: J. Yao et al., “Self-supervised amodal video object segmentation,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022. ista: 'Yao J, Hong Y, Wang C, Xiao T, He T, Locatello F, Wipf D, Fu Y, Zhang Z. 2022. Self-supervised amodal video object segmentation. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.' mla: Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” 36th Conference on Neural Information Processing Systems, 2022, doi:10.48550/arXiv.2210.12733. short: J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z. Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022. conference: end_date: 2022-12-01 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-21T12:13:25Z date_published: 2022-10-23T00:00:00Z date_updated: 2023-09-11T09:34:17Z day: '23' department: - _id: FrLo doi: 10.48550/arXiv.2210.12733 extern: '1' external_id: arxiv: - '2210.12733' fulldoi: https://doi.org/10.48550/arXiv.2210.12733 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2210.12733 month: '10' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: published status: public title: Self-supervised amodal video object segmentation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14114' abstract: - lang: eng text: Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups. article_processing_charge: No arxiv: 1 author: - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: Michael full_name: Lohaus, Michael last_name: Lohaus - first_name: Guha full_name: Balakrishnan, Guha last_name: Balakrishnan - first_name: Matthaus full_name: Kleindessner, Matthaus last_name: Kleindessner - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernhard full_name: Scholkopf, Bernhard last_name: Scholkopf - first_name: Chris full_name: Russell, Chris last_name: Russell citation: ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016' apa: 'Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Scholkopf, B., & Russell, C. (2022). Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016' chicago: 'Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner, Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10400–411. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01016.' ieee: 'D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.' ista: 'Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.' mla: 'Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.' short: D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411. conference: end_date: 2022-06-24 location: New Orleans, LA, United States name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2022-06-18 date_created: 2023-08-21T12:18:00Z date_published: 2022-07-01T00:00:00Z date_updated: 2023-09-11T09:19:14Z day: '01' department: - _id: FrLo doi: 10.1109/cvpr52688.2022.01016 extern: '1' external_id: arxiv: - '2203.04913' fulldoi: https://doi.org/10.1109/cvpr52688.2022.01016 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2203.04913 month: '07' oa: 1 oa_version: Preprint page: 10400-10411 publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_identifier: eissn: - 2575-7075 isbn: - '9781665469470' issn: - 1063-6919 publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14168' abstract: - lang: eng text: "Recent work has seen the development of general purpose neural architectures\r\nthat can be trained to perform tasks across diverse data modalities. General\r\npurpose models typically make few assumptions about the underlying\r\ndata-structure and are known to perform well in the large-data regime. At the\r\nsame time, there has been growing interest in modular neural architectures that\r\nrepresent the data using sparsely interacting modules. These models can be more\r\nrobust out-of-distribution, computationally efficient, and capable of\r\nsample-efficient adaptation to new data. However, they tend to make\r\ndomain-specific assumptions about the data, and present challenges in how\r\nmodule behavior (i.e., parameterization) and connectivity (i.e., their layout)\r\ncan be jointly learned. In this work, we introduce a general purpose, yet\r\nmodular neural architecture called Neural Attentive Circuits (NACs) that\r\njointly learns the parameterization and a sparse connectivity of neural modules\r\nwithout using domain knowledge. NACs are best understood as the combination of\r\ntwo systems that are jointly trained end-to-end: one that determines the module\r\nconfiguration and the other that executes it on an input. We demonstrate\r\nqualitatively that NACs learn diverse and meaningful module configurations on\r\nthe NLVR2 dataset without additional supervision. Quantitatively, we show that\r\nby incorporating modularity in this way, NACs improve upon a strong non-modular\r\nbaseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about\r\n10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that\r\nNACs can achieve an 8x speedup at inference time while losing less than 3%\r\nperformance. Finally, we find NACs to yield competitive results on diverse data\r\nmodalities spanning point-cloud classification, symbolic processing and\r\ntext-classification from ASCII bytes, thereby confirming its general purpose\r\nnature." alternative_title: - ' Advances in Neural Information Processing Systems' article_processing_charge: No arxiv: 1 author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Martin full_name: Weiss, Martin last_name: Weiss - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Chris full_name: Pal, Chris last_name: Pal - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Li Erran full_name: Li, Li Erran last_name: Li - first_name: Nicolas full_name: Ballas, Nicolas last_name: Ballas citation: ama: 'Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th Conference on Neural Information Processing Systems. Vol 35. ; 2022.' apa: Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B., … Ballas, N. (2022). Neural attentive circuits. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, United States. chicago: Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.” In 36th Conference on Neural Information Processing Systems, Vol. 35, 2022. ieee: N. Rahaman et al., “Neural attentive circuits,” in 36th Conference on Neural Information Processing Systems, New Orleans, United States, 2022, vol. 35. ista: 'Rahaman N, Weiss M, Locatello F, Pal C, Bengio Y, Schölkopf B, Li LE, Ballas N. 2022. Neural attentive circuits. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35.' mla: Rahaman, Nasim, et al. “Neural Attentive Circuits.” 36th Conference on Neural Information Processing Systems, vol. 35, 2022. short: N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E. Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems, 2022. conference: end_date: 2022-12-01 location: New Orleans, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-29 date_created: 2023-08-22T13:57:27Z date_published: 2022-10-14T00:00:00Z date_updated: 2023-09-11T09:29:09Z day: '14' department: - _id: FrLo extern: '1' external_id: arxiv: - '2210.08031' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2210.08031 month: '10' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: published status: public title: Neural attentive circuits type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ...