[{"author":[{"full_name":"Wu, Diyuan","first_name":"Diyuan","id":"1a5914c2-896a-11ed-bdf8-fb80621a0635","last_name":"Wu"},{"full_name":"Kungurtsev, Vyacheslav","last_name":"Kungurtsev","first_name":"Vyacheslav"},{"id":"27EB676C-8706-11E9-9510-7717E6697425","first_name":"Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","full_name":"Mondelli, Marco"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence","has_accepted_license":"1","publisher":"ML Research Press","language":[{"iso":"eng"}],"article_processing_charge":"No","department":[{"_id":"MaMo"}],"_id":"14924","quality_controlled":"1","citation":{"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.","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.","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.","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, .","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."},"arxiv":1,"abstract":[{"lang":"eng","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."}],"type":"conference","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"publication_status":"published","date_updated":"2024-09-10T13:03:20Z","oa":1,"alternative_title":["TMLR"],"date_published":"2023-02-28T00:00:00Z","month":"02","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\".","year":"2023","external_id":{"arxiv":["2210.06819"]},"license":"https://creativecommons.org/licenses/by/4.0/","date_created":"2024-02-02T11:21:56Z","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)"},"status":"public","day":"28","oa_version":"Published Version","publication":"Transactions on Machine Learning Research","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2210.06819","open_access":"1"}]},{"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."}],"arxiv":1,"type":"preprint","citation":{"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.","short":"D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, ArXiv (n.d.).","ieee":"D. Yao <i>et al.</i>, “Multi-view causal representation learning with partial observability,” <i>arXiv</i>. .","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>","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>.","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>","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>."},"status":"public","day":"07","date_created":"2024-02-07T14:28:34Z","_id":"14946","publication":"arXiv","publication_status":"submitted","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2311.04056"}],"doi":"10.48550/arXiv.2311.04056","oa_version":"Preprint","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.","article_number":"2311.04056","date_published":"2023-11-07T00:00:00Z","month":"11","oa":1,"author":[{"first_name":"Dingling","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","last_name":"Yao","full_name":"Yao, Dingling"},{"full_name":"Xu, Danru","last_name":"Xu","first_name":"Danru"},{"full_name":"Lachapelle, Sébastien","last_name":"Lachapelle","first_name":"Sébastien"},{"full_name":"Magliacane, Sara","last_name":"Magliacane","first_name":"Sara"},{"full_name":"Taslakian, Perouz","last_name":"Taslakian","first_name":"Perouz"},{"full_name":"Martius, Georg","last_name":"Martius","first_name":"Georg"},{"first_name":"Julius von","last_name":"Kügelgen","full_name":"Kügelgen, Julius von"},{"orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Multi-view causal representation learning with partial observability","date_updated":"2024-02-12T08:07:33Z","department":[{"_id":"FrLo"}],"external_id":{"arxiv":["2311.04056"]},"article_processing_charge":"No","year":"2023","language":[{"iso":"eng"}]},{"year":"2023","language":[{"iso":"eng"}],"department":[{"_id":"FrLo"}],"external_id":{"arxiv":["2307.09437"]},"article_processing_charge":"No","author":[{"full_name":"Kori, Avinash","last_name":"Kori","first_name":"Avinash"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Ribeiro, Fabio De Sousa","first_name":"Fabio De Sousa","last_name":"Ribeiro"},{"full_name":"Toni, Francesca","last_name":"Toni","first_name":"Francesca"},{"full_name":"Glocker, Ben","first_name":"Ben","last_name":"Glocker"}],"title":"Grounded object centric learning","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_updated":"2024-02-12T08:13:12Z","article_number":"2307.09437","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.","month":"07","date_published":"2023-07-18T00:00:00Z","oa_version":"Preprint","publication_status":"submitted","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2307.09437"}],"doi":"10.48550/arXiv.2307.09437","publication":"arXiv","day":"18","status":"public","_id":"14948","date_created":"2024-02-07T14:47:04Z","type":"preprint","abstract":[{"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.","lang":"eng"}],"arxiv":1,"citation":{"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>.","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>","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>.","ista":"Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. arXiv, 2307.09437.","short":"A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).","ieee":"A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded object centric learning,” <i>arXiv</i>. .","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>"}},{"article_type":"original","language":[{"iso":"eng"}],"department":[{"_id":"FrLo"}],"article_processing_charge":"No","title":"Image retrieval outperforms diffusion models on data augmentation","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","first_name":"Max","full_name":"Horn, Max"},{"full_name":"Makansi, Osama","last_name":"Makansi","first_name":"Osama"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"last_name":"Russell","first_name":"Chris","full_name":"Russell, Chris"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"file_size":27325153,"creator":"ptazenko","content_type":"application/pdf","file_id":"14950","relation":"main_file","checksum":"af87ddea7908923426365347b9c87ba7","access_level":"open_access","file_name":"Burg_et_al_2023_Image_retrieval_outperforms.pdf","date_created":"2024-02-07T14:57:32Z","date_updated":"2024-02-07T14:57:32Z"}],"publisher":"ML Research Press","has_accepted_license":"1","publication_status":"published","quality_controlled":"1","_id":"14949","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."}],"type":"journal_article","file_date_updated":"2024-02-07T14:57:32Z","publication_identifier":{"eissn":["2835-8856"]},"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.","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.","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.","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.","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.","mla":"Burg, Max, et al. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” <i>Journal of Machine Learning Research</i>, ML Research Press, 2023."},"year":"2023","oa":1,"date_updated":"2024-02-12T08:30:21Z","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).","date_published":"2023-12-10T00:00:00Z","alternative_title":["TMLR"],"month":"12","oa_version":"Published Version","publication":"Journal of Machine Learning Research","main_file_link":[{"url":"https://openreview.net/forum?id=xflYdGZMpv","open_access":"1"}],"day":"10","status":"public","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)"},"ddc":["000"],"date_created":"2024-02-07T14:57:39Z"},{"month":"11","date_published":"2023-11-01T00:00:00Z","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.","article_number":"2311.00664","date_updated":"2024-02-12T09:40:23Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"author":[{"full_name":"Maiorca, Valentino","first_name":"Valentino","last_name":"Maiorca"},{"first_name":"Luca","last_name":"Moschella","full_name":"Moschella, Luca"},{"full_name":"Norelli, Antonio","first_name":"Antonio","last_name":"Norelli"},{"full_name":"Fumero, Marco","last_name":"Fumero","first_name":"Marco"},{"last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"},{"last_name":"Rodolà","first_name":"Emanuele","full_name":"Rodolà, Emanuele"}],"title":"Latent space translation via semantic alignment","article_processing_charge":"No","external_id":{"arxiv":["2311.00664"]},"department":[{"_id":"FrLo"}],"language":[{"iso":"eng"}],"year":"2023","citation":{"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>","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>.","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>","ista":"Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. arXiv, 2311.00664.","ieee":"V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, and E. Rodolà, “Latent space translation via semantic alignment,” <i>arXiv</i>. .","short":"V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, E. Rodolà, ArXiv (n.d.).","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>."},"type":"preprint","arxiv":1,"abstract":[{"lang":"eng","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."}],"date_created":"2024-02-07T15:08:55Z","_id":"14952","day":"01","status":"public","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2311.00664","open_access":"1"}],"publication_status":"submitted","doi":"10.48550/arXiv.2311.00664","publication":"arXiv","oa_version":"Preprint"},{"type":"preprint","arxiv":1,"abstract":[{"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.","lang":"eng"}],"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>.","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>","ieee":"Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” <i>arXiv</i>. .","ista":"Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv, 2310.18123.","short":"Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).","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>.","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>"},"day":"27","status":"public","date_created":"2024-02-07T15:11:11Z","_id":"14953","doi":"10.48550/arXiv.2310.18123","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.18123","open_access":"1"}],"publication_status":"submitted","publication":"arXiv","oa_version":"Preprint","article_number":"2310.18123","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. ","month":"10","date_published":"2023-10-27T00:00:00Z","oa":1,"author":[{"full_name":"Zhu, Zhenyu","last_name":"Zhu","first_name":"Zhenyu"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"},{"full_name":"Cevher, Volkan","last_name":"Cevher","first_name":"Volkan"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Sample complexity bounds for score-matching: Causal discovery and generative modeling","date_updated":"2024-02-12T09:45:58Z","department":[{"_id":"FrLo"}],"external_id":{"arxiv":["2310.18123"]},"article_processing_charge":"No","year":"2023","language":[{"iso":"eng"}]},{"date_updated":"2024-02-12T09:51:15Z","title":"Assumption violations in causal discovery and the robustness of score matching","oa":1,"author":[{"full_name":"Montagna, Francesco","first_name":"Francesco","last_name":"Montagna"},{"last_name":"Mastakouri","first_name":"Atalanti A.","full_name":"Mastakouri, Atalanti A."},{"last_name":"Eulig","first_name":"Elias","full_name":"Eulig, Elias"},{"full_name":"Noceti, Nicoletta","last_name":"Noceti","first_name":"Nicoletta"},{"first_name":"Lorenzo","last_name":"Rosasco","full_name":"Rosasco, Lorenzo"},{"full_name":"Janzing, Dominik","last_name":"Janzing","first_name":"Dominik"},{"full_name":"Aragam, Bryon","last_name":"Aragam","first_name":"Bryon"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2023-10-20T00:00:00Z","month":"10","acknowledgement":"We thank Kun Zhang and Carl-Johann Simon-Gabriel for the insightful discussions. This work\r\nhas been supported by AFOSR, grant n. FA8655-20-1-7035. FM is supported by Programma\r\nOperativo Nazionale ricerca e innovazione 2014-2020. FM partially contributed to this work during an internship at Amazon Web Services with FL. FL partially contributed while at AWS.","article_number":"2310.13387","language":[{"iso":"eng"}],"year":"2023","article_processing_charge":"No","external_id":{"arxiv":["2310.13387"]},"department":[{"_id":"FrLo"}],"date_created":"2024-02-07T15:11:56Z","_id":"14954","day":"20","status":"public","citation":{"apa":"Montagna, F., Mastakouri, A. A., Eulig, E., Noceti, N., Rosasco, L., Janzing, D., … Locatello, F. (n.d.). Assumption violations in causal discovery and the robustness of score matching. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2310.13387\">https://doi.org/10.48550/arXiv.2310.13387</a>","mla":"Montagna, Francesco, et al. “Assumption Violations in Causal Discovery and the Robustness of Score Matching.” <i>ArXiv</i>, 2310.13387, doi:<a href=\"https://doi.org/10.48550/arXiv.2310.13387\">10.48550/arXiv.2310.13387</a>.","ieee":"F. Montagna <i>et al.</i>, “Assumption violations in causal discovery and the robustness of score matching,” <i>arXiv</i>. .","ista":"Montagna F, Mastakouri AA, Eulig E, Noceti N, Rosasco L, Janzing D, Aragam B, Locatello F. Assumption violations in causal discovery and the robustness of score matching. arXiv, 2310.13387.","short":"F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello, ArXiv (n.d.).","ama":"Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal discovery and the robustness of score matching. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2310.13387\">10.48550/arXiv.2310.13387</a>","chicago":"Montagna, Francesco, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, and Francesco Locatello. “Assumption Violations in Causal Discovery and the Robustness of Score Matching.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2310.13387\">https://doi.org/10.48550/arXiv.2310.13387</a>."},"arxiv":1,"abstract":[{"lang":"eng","text":"When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recover the causal structure, exploiting the statistical properties of their data. Because causal discovery without further assumptions is an ill-posed problem, each algorithm comes with its own set of\r\nusually untestable assumptions, some of which are hard to meet in real datasets. Motivated by these considerations, this paper extensively benchmarks the empirical performance of recent causal discovery methods on observational i.i.d. data generated under different background conditions, allowing for violations of the critical assumptions required by each selected approach. Our experimental findings show that score matching-based methods demonstrate\r\nsurprising performance in the false positive and false negative rate of the inferred graph in these challenging scenarios, and we provide theoretical insights into their performance. This work is also the first effort to benchmark the stability of causal discovery algorithms with respect to the values of their hyperparameters. Finally, we hope this paper will set a new standard for the evaluation of causal discovery methods and can serve as an accessible entry point for practitioners interested in the field, highlighting the empirical implications of different algorithm choices."}],"type":"preprint","oa_version":"Preprint","publication":"arXiv","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.13387","open_access":"1"}],"publication_status":"submitted","doi":"10.48550/arXiv.2310.13387"},{"article_processing_charge":"No","department":[{"_id":"FrLo"}],"language":[{"iso":"eng"}],"has_accepted_license":"1","publisher":"OpenReview","file":[{"relation":"main_file","checksum":"484efc27bda75ed6666044989695d9b6","access_level":"open_access","creator":"dernst","file_size":552357,"file_id":"14982","content_type":"application/pdf","date_updated":"2024-02-13T08:50:53Z","date_created":"2024-02-13T08:50:53Z","file_name":"2023_CRL_Xu.pdf","success":1}],"title":"A sparsity principle for partially observable causal representation learning","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Danru","last_name":"Xu","full_name":"Xu, Danru"},{"last_name":"Yao","first_name":"Dingling","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling"},{"full_name":"Lachapelle, Sebastien","last_name":"Lachapelle","first_name":"Sebastien"},{"full_name":"Taslakian, Perouz","last_name":"Taslakian","first_name":"Perouz"},{"last_name":"von Kügelgen","first_name":"Julius","full_name":"von Kügelgen, Julius"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"},{"last_name":"Magliacane","first_name":"Sara","full_name":"Magliacane, Sara"}],"publication_status":"published","citation":{"chicago":"Xu, Danru, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, and Sara Magliacane. “A Sparsity Principle for Partially Observable Causal Representation Learning.” In <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. OpenReview, 2023.","ama":"Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. OpenReview; 2023.","short":"D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview, 2023.","ista":"Xu D, Yao D, Lachapelle S, Taslakian P, von Kügelgen J, Locatello F, Magliacane S. 2023. A sparsity principle for partially observable causal representation learning. Causal Representation Learning Workshop at NeurIPS 2023. CRL: Causal Representation Learning Workshop at NeurIPS, 54.","ieee":"D. Xu <i>et al.</i>, “A sparsity principle for partially observable causal representation learning,” in <i>Causal Representation Learning Workshop at NeurIPS 2023</i>, New Orleans, LA, United States, 2023.","mla":"Xu, Danru, et al. “A Sparsity Principle for Partially Observable Causal Representation Learning.” <i>Causal Representation Learning Workshop at NeurIPS 2023</i>, 54, OpenReview, 2023.","apa":"Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., &#38; Magliacane, S. (2023). A sparsity principle for partially observable causal representation learning. In <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. New Orleans, LA, United States: OpenReview."},"type":"conference","file_date_updated":"2024-02-13T08:50:53Z","abstract":[{"lang":"eng","text":"Causal representation learning (CRL) aims at identifying high-level causal variables from low-level data, e.g. images. Current methods usually assume that all causal variables are captured in the high-dimensional observations. In this work, we focus on learning causal representations from data under partial observability, i.e., when some of the causal variables are not observed in the measurements, and the set of masked variables changes across the different samples. We introduce some initial theoretical results for identifying causal variables under partial observability by exploiting a sparsity regularizer, focusing in particular on the linear and piecewise linear mixing function case. We provide a theorem that allows us to identify the causal variables up to permutation and element-wise linear transformations in the linear case and a lemma that allows us to identify causal variables up to linear transformation in the piecewise case. Finally, we provide a conjecture that would allow us to identify the causal variables up to permutation and element-wise linear transformations also in the piecewise linear case. We test the theorem and conjecture on simulated data, showing the effectiveness of our method."}],"_id":"14958","quality_controlled":"1","conference":{"start_date":"2023-12-15","end_date":"2023-12-15","name":"CRL: Causal Representation Learning Workshop at NeurIPS","location":"New Orleans, LA, United States"},"year":"2023","month":"12","date_published":"2023-12-05T00:00:00Z","article_number":"54","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. 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 this 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. JvK acknowledges support from the German Federal Ministry of Education and Research (BMBF)\r\nthrough the Tübingen AI Center (FKZ: 01IS18039B).\r\n","date_updated":"2024-02-13T08:59:27Z","oa":1,"main_file_link":[{"url":"https://openreview.net/forum?id=Whr6uobelR","open_access":"1"}],"publication":"Causal Representation Learning Workshop at NeurIPS 2023","oa_version":"Published Version","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)"},"date_created":"2024-02-07T15:17:51Z","ddc":["000"],"status":"public","day":"05"},{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2310.14246"}],"doi":"10.48550/arXiv.2310.14246","publication_status":"submitted","publication":"arXiv","oa_version":"Preprint","citation":{"ama":"Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2310.14246\">10.48550/arXiv.2310.14246</a>","ista":"Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. arXiv, 2310.14246.","ieee":"F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal discovery of nonlinear models by score matching,” <i>arXiv</i>. .","short":"F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, and Francesco Locatello. “Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2310.14246\">https://doi.org/10.48550/arXiv.2310.14246</a>.","apa":"Montagna, F., Noceti, N., Rosasco, L., &#38; Locatello, F. (n.d.). Shortcuts for causal discovery of nonlinear models by score matching. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2310.14246\">https://doi.org/10.48550/arXiv.2310.14246</a>","mla":"Montagna, Francesco, et al. “Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.” <i>ArXiv</i>, 2310.14246, doi:<a href=\"https://doi.org/10.48550/arXiv.2310.14246\">10.48550/arXiv.2310.14246</a>."},"type":"preprint","abstract":[{"text":"The use of simulated data in the field of causal discovery is ubiquitous due to the scarcity of annotated real data. Recently, Reisach et al., 2021 highlighted the emergence of patterns in simulated linear data, which displays increasing marginal variance in the casual direction. As an ablation in their experiments, Montagna et al., 2023 found that similar patterns may emerge in\r\nnonlinear models for the variance of the score vector $\\nabla \\log p_{\\mathbf{X}}$, and introduced the ScoreSort algorithm. In this work, we formally define and characterize this score-sortability pattern of nonlinear additive noise models. We find that it defines a class of identifiable (bivariate) causal models overlapping with nonlinear additive noise models. We\r\ntheoretically demonstrate the advantages of ScoreSort in terms of statistical efficiency compared to prior state-of-the-art score matching-based methods and empirically show the score-sortability of the most common synthetic benchmarks in the literature. Our findings remark (1) the lack of diversity in the data as an important limitation in the evaluation of nonlinear causal discovery approaches, (2) the importance of thoroughly testing different settings within a problem class, and (3) the importance of analyzing statistical properties in\r\ncausal discovery, where research is often limited to defining identifiability conditions of the model. ","lang":"eng"}],"arxiv":1,"date_created":"2024-02-08T15:31:46Z","_id":"14961","day":"22","status":"public","external_id":{"arxiv":["2310.14246"]},"article_processing_charge":"No","department":[{"_id":"FrLo"}],"language":[{"iso":"eng"}],"year":"2023","month":"10","date_published":"2023-10-22T00:00:00Z","article_number":"2310.14246","date_updated":"2024-02-12T10:03:33Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Shortcuts for causal discovery of nonlinear models by score matching","author":[{"full_name":"Montagna, Francesco","first_name":"Francesco","last_name":"Montagna"},{"full_name":"Noceti, Nicoletta","last_name":"Noceti","first_name":"Nicoletta"},{"first_name":"Lorenzo","last_name":"Rosasco","full_name":"Rosasco, Lorenzo"},{"orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"}],"oa":1},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"title":"Unsupervised open-vocabulary object localization in videos","author":[{"full_name":"Fan, Ke","last_name":"Fan","first_name":"Ke"},{"full_name":"Bai, Zechen","last_name":"Bai","first_name":"Zechen"},{"last_name":"Xiao","first_name":"Tianjun","full_name":"Xiao, Tianjun"},{"last_name":"Zietlow","first_name":"Dominik","full_name":"Zietlow, Dominik"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"last_name":"Zhao","first_name":"Zixu","full_name":"Zhao, Zixu"},{"full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel","first_name":"Carl-Johann Simon-Gabriel","last_name":"Carl-Johann Simon-Gabriel"},{"full_name":"Shou, Mike Zheng","first_name":"Mike Zheng","last_name":"Shou"},{"full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"},{"last_name":"Schiele","first_name":"Bernt","full_name":"Schiele, Bernt"},{"last_name":"Brox","first_name":"Thomas","full_name":"Brox, Thomas"},{"full_name":"Zhang, Zheng","first_name":"Zheng","last_name":"Zhang"},{"full_name":"Fu, Yanwei","last_name":"Fu","first_name":"Yanwei"},{"last_name":"He","first_name":"Tong","full_name":"He, Tong"}],"date_updated":"2024-02-12T10:12:22Z","article_number":"2309.09858","date_published":"2023-09-18T00:00:00Z","month":"09","year":"2023","language":[{"iso":"eng"}],"department":[{"_id":"FrLo"}],"article_processing_charge":"No","external_id":{"arxiv":["2309.09858"]},"status":"public","day":"18","date_created":"2024-02-08T15:33:39Z","_id":"14962","arxiv":1,"abstract":[{"lang":"eng","text":"In this paper, we show that recent advances in video representation learning\r\nand pre-trained vision-language models allow for substantial improvements in\r\nself-supervised video object localization. We propose a method that first\r\nlocalizes objects in videos via a slot attention approach and then assigns text\r\nto the obtained slots. The latter is achieved by an unsupervised way to read\r\nlocalized semantic information from the pre-trained CLIP model. The resulting\r\nvideo object localization is entirely unsupervised apart from the implicit\r\nannotation contained in CLIP, and it is effectively the first unsupervised\r\napproach that yields good results on regular video benchmarks."}],"type":"preprint","extern":"1","citation":{"apa":"Fan, K., Bai, Z., Xiao, T., Zietlow, D., Horn, M., Zhao, Z., … He, T. (n.d.). Unsupervised open-vocabulary object localization in videos. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2309.09858\">https://doi.org/10.48550/arXiv.2309.09858</a>","mla":"Fan, Ke, et al. “Unsupervised Open-Vocabulary Object Localization in Videos.” <i>ArXiv</i>, 2309.09858, doi:<a href=\"https://doi.org/10.48550/arXiv.2309.09858\">10.48550/arXiv.2309.09858</a>.","ama":"Fan K, Bai Z, Xiao T, et al. Unsupervised open-vocabulary object localization in videos. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2309.09858\">10.48550/arXiv.2309.09858</a>","ista":"Fan K, Bai Z, Xiao T, Zietlow D, Horn M, Zhao Z, Carl-Johann Simon-Gabriel C-JS-G, Shou MZ, Locatello F, Schiele B, Brox T, Zhang Z, Fu Y, He T. Unsupervised open-vocabulary object localization in videos. arXiv, 2309.09858.","short":"K. Fan, Z. Bai, T. Xiao, D. Zietlow, M. Horn, Z. Zhao, C.-J.S.-G. Carl-Johann Simon-Gabriel, M.Z. Shou, F. Locatello, B. Schiele, T. Brox, Z. Zhang, Y. Fu, T. He, ArXiv (n.d.).","ieee":"K. Fan <i>et al.</i>, “Unsupervised open-vocabulary object localization in videos,” <i>arXiv</i>. .","chicago":"Fan, Ke, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, et al. “Unsupervised Open-Vocabulary Object Localization in Videos.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2309.09858\">https://doi.org/10.48550/arXiv.2309.09858</a>."},"oa_version":"Preprint","publication":"arXiv","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2309.09858"}],"publication_status":"submitted","doi":"10.48550/arXiv.2309.09858"},{"oa":1,"title":"Object-centric multiple object tracking","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Zhao, Zixu","first_name":"Zixu","last_name":"Zhao"},{"last_name":"Wang","first_name":"Jiaze","full_name":"Wang, Jiaze"},{"full_name":"Horn, Max","first_name":"Max","last_name":"Horn"},{"full_name":"Ding, Yizhuo","first_name":"Yizhuo","last_name":"Ding"},{"last_name":"He","first_name":"Tong","full_name":"He, Tong"},{"full_name":"Bai, Zechen","last_name":"Bai","first_name":"Zechen"},{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel","last_name":"Carl-Johann Simon-Gabriel","first_name":"Carl-Johann Simon-Gabriel"},{"first_name":"Bing","last_name":"Shuai","full_name":"Shuai, Bing"},{"last_name":"Tu","first_name":"Zhuowen","full_name":"Tu, Zhuowen"},{"last_name":"Brox","first_name":"Thomas","full_name":"Brox, Thomas"},{"full_name":"Schiele, Bernt","first_name":"Bernt","last_name":"Schiele"},{"last_name":"Fu","first_name":"Yanwei","full_name":"Fu, Yanwei"},{"last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"},{"full_name":"Zhang, Zheng","last_name":"Zhang","first_name":"Zheng"},{"full_name":"Xiao, Tianjun","first_name":"Tianjun","last_name":"Xiao"}],"date_updated":"2024-02-12T10:16:21Z","article_number":"2309.00233","date_published":"2023-09-01T00:00:00Z","month":"09","year":"2023","language":[{"iso":"eng"}],"department":[{"_id":"FrLo"}],"article_processing_charge":"No","external_id":{"arxiv":["2309.00233"]},"day":"01","status":"public","date_created":"2024-02-08T15:34:43Z","_id":"14963","abstract":[{"text":"Unsupervised object-centric learning methods allow the partitioning of scenes\r\ninto entities without additional localization information and are excellent\r\ncandidates for reducing the annotation burden of multiple-object tracking (MOT)\r\npipelines. Unfortunately, they lack two key properties: objects are often split\r\ninto parts and are not consistently tracked over time. In fact,\r\nstate-of-the-art models achieve pixel-level accuracy and temporal consistency\r\nby relying on supervised object detection with additional ID labels for the\r\nassociation through time. This paper proposes a video object-centric model for\r\nMOT. It consists of an index-merge module that adapts the object-centric slots\r\ninto detection outputs and an object memory module that builds complete object\r\nprototypes to handle occlusions. Benefited from object-centric learning, we\r\nonly require sparse detection labels (0%-6.25%) for object localization and\r\nfeature binding. Relying on our self-supervised\r\nExpectation-Maximization-inspired loss for object association, our approach\r\nrequires no ID labels. Our experiments significantly narrow the gap between the\r\nexisting object-centric model and the fully supervised state-of-the-art and\r\noutperform several unsupervised trackers.","lang":"eng"}],"arxiv":1,"type":"preprint","citation":{"ista":"Zhao Z, Wang J, Horn M, Ding Y, He T, Bai Z, Zietlow D, Carl-Johann Simon-Gabriel C-JS-G, Shuai B, Tu Z, Brox T, Schiele B, Fu Y, Locatello F, Zhang Z, Xiao T. Object-centric multiple object tracking. arXiv, 2309.00233.","ieee":"Z. Zhao <i>et al.</i>, “Object-centric multiple object tracking,” <i>arXiv</i>. .","short":"Z. Zhao, J. Wang, M. Horn, Y. Ding, T. He, Z. Bai, D. Zietlow, C.-J.S.-G. Carl-Johann Simon-Gabriel, B. Shuai, Z. Tu, T. Brox, B. Schiele, Y. Fu, F. Locatello, Z. Zhang, T. Xiao, ArXiv (n.d.).","ama":"Zhao Z, Wang J, Horn M, et al. Object-centric multiple object tracking. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2309.00233\">10.48550/arXiv.2309.00233</a>","chicago":"Zhao, Zixu, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik Zietlow, et al. “Object-Centric Multiple Object Tracking.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2309.00233\">https://doi.org/10.48550/arXiv.2309.00233</a>.","apa":"Zhao, Z., Wang, J., Horn, M., Ding, Y., He, T., Bai, Z., … Xiao, T. (n.d.). Object-centric multiple object tracking. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2309.00233\">https://doi.org/10.48550/arXiv.2309.00233</a>","mla":"Zhao, Zixu, et al. “Object-Centric Multiple Object Tracking.” <i>ArXiv</i>, 2309.00233, doi:<a href=\"https://doi.org/10.48550/arXiv.2309.00233\">10.48550/arXiv.2309.00233</a>."},"extern":"1","oa_version":"Preprint","publication":"arXiv","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2309.00233"}],"publication_status":"submitted","doi":"10.48550/arXiv.2309.00233"},{"volume":2,"day":"01","status":"public","date_created":"2024-02-14T12:12:17Z","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)"},"ddc":["540"],"doi":"10.1002/idm2.12056","publication":"Interdisciplinary Materials","oa_version":"Published Version","acknowledgement":"The authors would like to acknowledge the strong supportof microstructure observation from Center for HighPressure Science and Technology Advanced Research(HPSTAR). We acknowledge the financial support fromthe  National  Natural  Science  Foundation  of  China:52172236, the Fundamental Research Funds for theCentral Universities: xtr042021007, Top Young TalentsProgramme of Xi'an Jiaotong University and NationalScience Fund for Distinguished Young Scholars: 51925101.","month":"01","intvolume":"         2","date_published":"2023-01-01T00:00:00Z","issue":"1","oa":1,"date_updated":"2024-02-19T10:01:26Z","page":"161-170","year":"2023","type":"journal_article","file_date_updated":"2024-02-19T09:58:32Z","abstract":[{"lang":"eng","text":"Lead sulfide (PbS) presents large potential in thermoelectric application due to its earth-abundant S element. However, its inferior average ZT (ZTave) value makes PbS less competitive with its analogs PbTe and PbSe. To promote its thermoelectric performance, this study implements strategies of continuous Se alloying and Cu interstitial doping to synergistically tune thermal and electrical transport properties in n-type PbS. First, the lattice parameter of 5.93 Å in PbS is linearly expanded to 6.03 Å in PbS0.5Se0.5 with increasing Se alloying content. This expanded lattice in Se-alloyed PbS not only intensifies phonon scattering but also facilitates the formation of Cu interstitials. Based on the PbS0.6Se0.4 content with the minimal lattice thermal conductivity, Cu interstitials are introduced to improve the electron density, thus boosting the peak power factor, from 3.88 μW cm−1 K−2 in PbS0.6Se0.4 to 20.58 μW cm−1 K−2 in PbS0.6Se0.4−1%Cu. Meanwhile, the lattice thermal conductivity in PbS0.6Se0.4−x%Cu (x = 0–2) is further suppressed due to the strong strain field caused by Cu interstitials. Finally, with the lowered thermal conductivity and high electrical transport properties, a peak ZT ~1.1 and ZTave ~0.82 can be achieved in PbS0.6Se0.4 − 1%Cu at 300–773K, which outperforms previously reported n-type PbS."}],"citation":{"ista":"Liu Z, Hong T, Xu L, Wang S, Gao X, Chang C, Ding X, Xiao Y, Zhao L. 2023. Lattice expansion enables interstitial doping to achieve a high average ZT in n‐type PbS. Interdisciplinary Materials. 2(1), 161–170.","short":"Z. Liu, T. Hong, L. Xu, S. Wang, X. Gao, C. Chang, X. Ding, Y. Xiao, L. Zhao, Interdisciplinary Materials 2 (2023) 161–170.","ieee":"Z. Liu <i>et al.</i>, “Lattice expansion enables interstitial doping to achieve a high average ZT in n‐type PbS,” <i>Interdisciplinary Materials</i>, vol. 2, no. 1. Wiley, pp. 161–170, 2023.","ama":"Liu Z, Hong T, Xu L, et al. Lattice expansion enables interstitial doping to achieve a high average ZT in n‐type PbS. <i>Interdisciplinary Materials</i>. 2023;2(1):161-170. doi:<a href=\"https://doi.org/10.1002/idm2.12056\">10.1002/idm2.12056</a>","chicago":"Liu, Zhengtao, Tao Hong, Liqing Xu, Sining Wang, Xiang Gao, Cheng Chang, Xiangdong Ding, Yu Xiao, and Li‐Dong Zhao. “Lattice Expansion Enables Interstitial Doping to Achieve a High Average ZT in N‐type PbS.” <i>Interdisciplinary Materials</i>. Wiley, 2023. <a href=\"https://doi.org/10.1002/idm2.12056\">https://doi.org/10.1002/idm2.12056</a>.","apa":"Liu, Z., Hong, T., Xu, L., Wang, S., Gao, X., Chang, C., … Zhao, L. (2023). Lattice expansion enables interstitial doping to achieve a high average ZT in n‐type PbS. <i>Interdisciplinary Materials</i>. Wiley. <a href=\"https://doi.org/10.1002/idm2.12056\">https://doi.org/10.1002/idm2.12056</a>","mla":"Liu, Zhengtao, et al. “Lattice Expansion Enables Interstitial Doping to Achieve a High Average ZT in N‐type PbS.” <i>Interdisciplinary Materials</i>, vol. 2, no. 1, Wiley, 2023, pp. 161–70, doi:<a href=\"https://doi.org/10.1002/idm2.12056\">10.1002/idm2.12056</a>."},"publication_identifier":{"eissn":["2767-441X"]},"quality_controlled":"1","_id":"14985","publication_status":"published","publisher":"Wiley","has_accepted_license":"1","file":[{"access_level":"open_access","relation":"main_file","checksum":"7b5e8210ef1434feb173022c6dbbee0c","creator":"dernst","file_size":4675941,"file_id":"15015","content_type":"application/pdf","date_updated":"2024-02-19T09:58:32Z","date_created":"2024-02-19T09:58:32Z","file_name":"2023_InterdiscMaterials_Liu.pdf","success":1}],"author":[{"first_name":"Zhengtao","last_name":"Liu","full_name":"Liu, Zhengtao"},{"full_name":"Hong, Tao","first_name":"Tao","last_name":"Hong"},{"last_name":"Xu","first_name":"Liqing","full_name":"Xu, Liqing"},{"last_name":"Wang","first_name":"Sining","full_name":"Wang, Sining"},{"full_name":"Gao, Xiang","last_name":"Gao","first_name":"Xiang"},{"id":"9E331C2E-9F27-11E9-AE48-5033E6697425","first_name":"Cheng","last_name":"Chang","orcid":"0000-0002-9515-4277","full_name":"Chang, Cheng"},{"first_name":"Xiangdong","last_name":"Ding","full_name":"Ding, Xiangdong"},{"first_name":"Yu","last_name":"Xiao","full_name":"Xiao, Yu"},{"first_name":"Li‐Dong","last_name":"Zhao","full_name":"Zhao, Li‐Dong"}],"title":"Lattice expansion enables interstitial doping to achieve a high average ZT in n‐type PbS","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"MaIb"}],"article_processing_charge":"Yes","article_type":"original","language":[{"iso":"eng"}]},{"citation":{"apa":"Malvai, H., Kokoris Kogias, E., Sonnino, A., Ghosh, E., Oztürk, E., Lewi, K., &#38; Lawlor, S. (2023). Parakeet: Practical key transparency for end-to-end eEncrypted messaging. In <i>Proceedings of the 2023 Network and Distributed System Security Symposium</i>. San Diego, CA, United States: Internet Society. <a href=\"https://doi.org/10.14722/ndss.2023.24545\">https://doi.org/10.14722/ndss.2023.24545</a>","mla":"Malvai, Harjasleen, et al. “Parakeet: Practical Key Transparency for End-to-End EEncrypted Messaging.” <i>Proceedings of the 2023 Network and Distributed System Security Symposium</i>, Internet Society, 2023, doi:<a href=\"https://doi.org/10.14722/ndss.2023.24545\">10.14722/ndss.2023.24545</a>.","ama":"Malvai H, Kokoris Kogias E, Sonnino A, et al. Parakeet: Practical key transparency for end-to-end eEncrypted messaging. In: <i>Proceedings of the 2023 Network and Distributed System Security Symposium</i>. Internet Society; 2023. doi:<a href=\"https://doi.org/10.14722/ndss.2023.24545\">10.14722/ndss.2023.24545</a>","ista":"Malvai H, Kokoris Kogias E, Sonnino A, Ghosh E, Oztürk E, Lewi K, Lawlor S. 2023. Parakeet: Practical key transparency for end-to-end eEncrypted messaging. Proceedings of the 2023 Network and Distributed System Security Symposium. NDSS: Network and Distributed Systems Security.","short":"H. Malvai, E. Kokoris Kogias, A. Sonnino, E. Ghosh, E. Oztürk, K. Lewi, S. Lawlor, in:, Proceedings of the 2023 Network and Distributed System Security Symposium, Internet Society, 2023.","ieee":"H. Malvai <i>et al.</i>, “Parakeet: Practical key transparency for end-to-end eEncrypted messaging,” in <i>Proceedings of the 2023 Network and Distributed System Security Symposium</i>, San Diego, CA, United States, 2023.","chicago":"Malvai, Harjasleen, Eleftherios Kokoris Kogias, Alberto Sonnino, Esha Ghosh, Ercan Oztürk, Kevin Lewi, and Sean Lawlor. “Parakeet: Practical Key Transparency for End-to-End EEncrypted Messaging.” In <i>Proceedings of the 2023 Network and Distributed System Security Symposium</i>. Internet Society, 2023. <a href=\"https://doi.org/10.14722/ndss.2023.24545\">https://doi.org/10.14722/ndss.2023.24545</a>."},"publication_identifier":{"isbn":["1891562835"]},"type":"conference","abstract":[{"lang":"eng","text":"Encryption alone is not enough for secure end-to end encrypted messaging: a server must also honestly serve public keys to users. Key transparency has been presented as an efficient\r\nsolution for detecting (and hence deterring) a server that attempts to dishonestly serve keys. Key transparency involves two major components: (1) a username to public key mapping, stored and cryptographically committed to by the server, and, (2) an outof-band consistency protocol for serving short commitments to users. In the setting of real-world deployments and supporting production scale, new challenges must be considered for both of these components. We enumerate these challenges and provide solutions to address them. In particular, we design and implement a memory-optimized and privacy-preserving verifiable data structure for committing to the username to public key store.\r\nTo make this implementation viable for production, we also integrate support for persistent and distributed storage. We also propose a future-facing solution, termed “compaction”, as\r\na mechanism for mitigating practical issues that arise from dealing with infinitely growing server data structures. Finally, we implement a consensusless solution that achieves the minimum requirements for a service that consistently distributes commitments for a transparency application, providing a much more efficient protocol for distributing small and consistent\r\ncommitments to users. This culminates in our production-grade implementation of a key transparency system (Parakeet) which we have open-sourced, along with a demonstration of feasibility through our benchmarks."}],"date_created":"2024-02-14T14:20:40Z","_id":"14989","quality_controlled":"1","status":"public","day":"01","doi":"10.14722/ndss.2023.24545","main_file_link":[{"open_access":"1","url":"https://eprint.iacr.org/2023/081"}],"publication_status":"published","publication":"Proceedings of the 2023 Network and Distributed System Security Symposium","oa_version":"Published Version","month":"03","date_published":"2023-03-01T00:00:00Z","publisher":"Internet Society","acknowledgement":"This work is supported by the Novi team at Meta and funded in part by IC3 industry partners and NSF grant 1943499.","date_updated":"2024-02-19T12:11:15Z","oa":1,"author":[{"last_name":"Malvai","first_name":"Harjasleen","full_name":"Malvai, Harjasleen"},{"last_name":"Kokoris Kogias","id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30","first_name":"Eleftherios","full_name":"Kokoris Kogias, Eleftherios"},{"last_name":"Sonnino","first_name":"Alberto","full_name":"Sonnino, Alberto"},{"full_name":"Ghosh, Esha","first_name":"Esha","last_name":"Ghosh"},{"first_name":"Ercan","last_name":"Oztürk","full_name":"Oztürk, Ercan"},{"last_name":"Lewi","first_name":"Kevin","full_name":"Lewi, Kevin"},{"first_name":"Sean","last_name":"Lawlor","full_name":"Lawlor, Sean"}],"title":"Parakeet: Practical key transparency for end-to-end eEncrypted messaging","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","department":[{"_id":"ElKo"}],"conference":{"location":"San Diego, CA, United States","start_date":"2023-02-27","end_date":"2023-03-03","name":"NDSS: Network and Distributed Systems Security"},"language":[{"iso":"eng"}],"year":"2023"},{"type":"research_data_reference","abstract":[{"text":"The software artefact to evaluate the approximation of stationary distributions implementation.","lang":"eng"}],"citation":{"apa":"Meggendorfer, T. (2023). Artefact for: Correct Approximation of Stationary Distributions. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.7548214\">https://doi.org/10.5281/ZENODO.7548214</a>","mla":"Meggendorfer, Tobias. <i>Artefact for: Correct Approximation of Stationary Distributions</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.7548214\">10.5281/ZENODO.7548214</a>.","ama":"Meggendorfer T. Artefact for: Correct Approximation of Stationary Distributions. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.7548214\">10.5281/ZENODO.7548214</a>","ista":"Meggendorfer T. 2023. Artefact for: Correct Approximation of Stationary Distributions, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.7548214\">10.5281/ZENODO.7548214</a>.","short":"T. Meggendorfer, (2023).","ieee":"T. Meggendorfer, “Artefact for: Correct Approximation of Stationary Distributions.” Zenodo, 2023.","chicago":"Meggendorfer, Tobias. “Artefact for: Correct Approximation of Stationary Distributions.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.7548214\">https://doi.org/10.5281/ZENODO.7548214</a>."},"day":"18","status":"public","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)"},"date_created":"2024-02-14T14:27:06Z","_id":"14990","ddc":["000"],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.7548214"}],"doi":"10.5281/ZENODO.7548214","oa_version":"Published Version","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"13139"}]},"publisher":"Zenodo","month":"01","has_accepted_license":"1","date_published":"2023-01-18T00:00:00Z","title":"Artefact for: Correct Approximation of Stationary Distributions","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"author":[{"full_name":"Meggendorfer, Tobias","id":"b21b0c15-30a2-11eb-80dc-f13ca25802e1","first_name":"Tobias","orcid":"0000-0002-1712-2165","last_name":"Meggendorfer"}],"date_updated":"2024-02-27T07:19:32Z","department":[{"_id":"KrCh"}],"article_processing_charge":"No","year":"2023"},{"date_updated":"2024-02-27T07:26:31Z","title":"Data-assessing memory in convection schemes using idealized tests","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"author":[{"full_name":"Hwong, Yi-Ling","last_name":"Hwong","orcid":"0000-0001-9281-3479","first_name":"Yi-Ling","id":"1217aa61-4dd1-11ec-9ac3-f2ba3f17ee22"},{"full_name":"Colin, Maxime","last_name":"Colin","first_name":"Maxime"},{"full_name":"Aglas, Philipp","last_name":"Aglas","id":"02eace56-97fc-11ee-b81a-f0939ca85a77","first_name":"Philipp"},{"first_name":"Caroline J","id":"f978ccb0-3f7f-11eb-b193-b0e2bd13182b","orcid":"0000-0001-5836-5350","last_name":"Muller","full_name":"Muller, Caroline J"},{"first_name":"Steven C.","last_name":"Sherwood","full_name":"Sherwood, Steven C."}],"has_accepted_license":"1","month":"06","date_published":"2023-06-23T00:00:00Z","publisher":"Zenodo","year":"2023","article_processing_charge":"No","department":[{"_id":"CaMu"}],"_id":"14991","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)"},"date_created":"2024-02-14T14:37:57Z","ddc":["550"],"day":"23","status":"public","citation":{"apa":"Hwong, Y.-L., Colin, M., Aglas, P., Muller, C. J., &#38; Sherwood, S. C. (2023). Data-assessing memory in convection schemes using idealized tests. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.7757041\">https://doi.org/10.5281/ZENODO.7757041</a>","mla":"Hwong, Yi-Ling, et al. <i>Data-Assessing Memory in Convection Schemes Using Idealized Tests</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.7757041\">10.5281/ZENODO.7757041</a>.","ama":"Hwong Y-L, Colin M, Aglas P, Muller CJ, Sherwood SC. Data-assessing memory in convection schemes using idealized tests. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.7757041\">10.5281/ZENODO.7757041</a>","ista":"Hwong Y-L, Colin M, Aglas P, Muller CJ, Sherwood SC. 2023. Data-assessing memory in convection schemes using idealized tests, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.7757041\">10.5281/ZENODO.7757041</a>.","short":"Y.-L. Hwong, M. Colin, P. Aglas, C.J. Muller, S.C. Sherwood, (2023).","ieee":"Y.-L. Hwong, M. Colin, P. Aglas, C. J. Muller, and S. C. Sherwood, “Data-assessing memory in convection schemes using idealized tests.” Zenodo, 2023.","chicago":"Hwong, Yi-Ling, Maxime Colin, Philipp Aglas, Caroline J Muller, and Steven C. Sherwood. “Data-Assessing Memory in Convection Schemes Using Idealized Tests.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.7757041\">https://doi.org/10.5281/ZENODO.7757041</a>."},"type":"research_data_reference","abstract":[{"text":"This repository contains the data, scripts, WRF codes and files required to reproduce the results of the manuscript \"Assessing Memory in Convection Schemes Using Idealized Tests\" submitted to the Journal of Advances in Modeling Earth Systems (JAMES).","lang":"eng"}],"related_material":{"record":[{"id":"14654","status":"public","relation":"used_in_publication"}]},"ec_funded":1,"oa_version":"Published Version","project":[{"grant_number":"101034413","call_identifier":"H2020","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.7757041"}],"doi":"10.5281/ZENODO.7757041"},{"publisher":"Springer","edition":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Universal Functionals in Density Functional Theory","author":[{"full_name":"Lewin, Mathieu","last_name":"Lewin","first_name":"Mathieu"},{"full_name":"Lieb, Elliott H.","last_name":"Lieb","first_name":"Elliott H."},{"full_name":"Seiringer, Robert","orcid":"0000-0002-6781-0521","last_name":"Seiringer","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","first_name":"Robert"}],"department":[{"_id":"RoSe"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"arxiv":1,"abstract":[{"lang":"eng","text":"In this chapter we first review the Levy–Lieb functional, which gives the lowest kinetic and interaction energy that can be reached with all possible quantum states having a given density. We discuss two possible convex generalizations of this functional, corresponding to using mixed canonical and grand-canonical states, respectively. We present some recent works about the local density approximation, in which the functionals get replaced by purely local functionals constructed using the uniform electron gas energy per unit volume. We then review the known upper and lower bounds on the Levy–Lieb functionals. We start with the kinetic energy alone, then turn to the classical interaction alone, before we are able to put everything together. A later section is devoted to the Hohenberg–Kohn theorem and the role of many-body unique continuation in its proof."}],"type":"book_chapter","citation":{"mla":"Lewin, Mathieu, et al. “Universal Functionals in Density Functional Theory.” <i>Density Functional Theory</i>, edited by Eric Cances and Gero Friesecke, 1st ed., Springer, 2023, pp. 115–82, doi:<a href=\"https://doi.org/10.1007/978-3-031-22340-2_3\">10.1007/978-3-031-22340-2_3</a>.","apa":"Lewin, M., Lieb, E. H., &#38; Seiringer, R. (2023). Universal Functionals in Density Functional Theory. In E. Cances &#38; G. Friesecke (Eds.), <i>Density Functional Theory</i> (1st ed., pp. 115–182). Springer. <a href=\"https://doi.org/10.1007/978-3-031-22340-2_3\">https://doi.org/10.1007/978-3-031-22340-2_3</a>","chicago":"Lewin, Mathieu, Elliott H. Lieb, and Robert Seiringer. “Universal Functionals in Density Functional Theory.” In <i>Density Functional Theory</i>, edited by Eric Cances and Gero Friesecke, 1st ed., 115–82. MAMOMO. Springer, 2023. <a href=\"https://doi.org/10.1007/978-3-031-22340-2_3\">https://doi.org/10.1007/978-3-031-22340-2_3</a>.","ieee":"M. Lewin, E. H. Lieb, and R. Seiringer, “Universal Functionals in Density Functional Theory,” in <i>Density Functional Theory</i>, 1st ed., E. Cances and G. Friesecke, Eds. Springer, 2023, pp. 115–182.","short":"M. Lewin, E.H. Lieb, R. Seiringer, in:, E. Cances, G. Friesecke (Eds.), Density Functional Theory, 1st ed., Springer, 2023, pp. 115–182.","ista":"Lewin M, Lieb EH, Seiringer R. 2023.Universal Functionals in Density Functional Theory. In: Density Functional Theory. Mathematics and Molecular Modeling, , 115–182.","ama":"Lewin M, Lieb EH, Seiringer R. Universal Functionals in Density Functional Theory. In: Cances E, Friesecke G, eds. <i>Density Functional Theory</i>. 1st ed. MAMOMO. Springer; 2023:115-182. doi:<a href=\"https://doi.org/10.1007/978-3-031-22340-2_3\">10.1007/978-3-031-22340-2_3</a>"},"publication_identifier":{"eisbn":["9783031223402"],"isbn":["9783031223396"],"issn":["3005-0286"]},"quality_controlled":"1","_id":"14992","editor":[{"last_name":"Cances","first_name":"Eric","full_name":"Cances, Eric"},{"full_name":"Friesecke, Gero","last_name":"Friesecke","first_name":"Gero"}],"publication_status":"published","date_published":"2023-07-19T00:00:00Z","alternative_title":["Mathematics and Molecular Modeling"],"month":"07","oa":1,"date_updated":"2024-02-20T08:33:06Z","page":"115-182","external_id":{"arxiv":["1912.10424"]},"series_title":"MAMOMO","year":"2023","status":"public","day":"19","date_created":"2024-02-14T14:44:33Z","publication":"Density Functional Theory","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1912.10424"}],"doi":"10.1007/978-3-031-22340-2_3","oa_version":"Preprint"},{"citation":{"ieee":"C. Currin <i>et al.</i>, “A framework for grassroots research collaboration in machine learning and global health,” in <i>1st Workshop on Machine Learning &#38; Global Health</i>, Kigali, Rwanda, 2023.","ista":"Currin C, Asiedu  MN, Fourie C, Rosman B, Turki H, Lambebo Tonja A, Abbott J, Ajala M, Adedayo SA, Emezue CC, Machangara D. 2023. A framework for grassroots research collaboration in machine learning and global health. 1st Workshop on Machine Learning &#38; Global Health. ICLR: International Conference on Learning Representations.","short":"C. Currin, M.N. Asiedu , C. Fourie, B. Rosman, H. Turki, A. Lambebo Tonja, J. Abbott, M. Ajala, S.A. Adedayo, C.C. Emezue, D. Machangara, in:, 1st Workshop on Machine Learning &#38; Global Health, OpenReview, 2023.","ama":"Currin C, Asiedu  MN, Fourie C, et al. A framework for grassroots research collaboration in machine learning and global health. In: <i>1st Workshop on Machine Learning &#38; Global Health</i>. OpenReview; 2023.","chicago":"Currin, Christopher, Mercy Nyamewaa Asiedu , Chris Fourie, Benjamin Rosman, Houcemeddine Turki, Atnafu Lambebo Tonja, Jade Abbott, et al. “A Framework for Grassroots Research Collaboration in Machine Learning and Global Health.” In <i>1st Workshop on Machine Learning &#38; Global Health</i>. OpenReview, 2023.","apa":"Currin, C., Asiedu , M. N., Fourie, C., Rosman, B., Turki, H., Lambebo Tonja, A., … Machangara, D. (2023). A framework for grassroots research collaboration in machine learning and global health. In <i>1st Workshop on Machine Learning &#38; Global Health</i>. Kigali, Rwanda: OpenReview.","mla":"Currin, Christopher, et al. “A Framework for Grassroots Research Collaboration in Machine Learning and Global Health.” <i>1st Workshop on Machine Learning &#38; Global Health</i>, OpenReview, 2023."},"abstract":[{"lang":"eng","text":"Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015). Recently, however, a more holistic, multi-level approach termed One Health (OH) (Osterhaus et al., 2020) is being adopted. Several sets of challenges have been identified for the implementation of OH (dos S. Ribeiro et al., 2019), including policy and funding, education and training, and multi-actor, multi-domain, and multi-level collaborations. These exist despite the increasing accessibility to\r\nknowledge and digital collaborative research tools through the internet. To address some of these challenges, we propose a general framework for grassroots community-based means of participatory research. Additionally, we present a specific roadmap to create a Machine Learning for Global Health community in Africa. The proposed framework aims to enable any small group of individuals with scarce resources to build and sustain an online community within approximately two years. We provide a discussion on the potential impact of the proposed framework for global health research collaborations."}],"type":"conference","date_created":"2024-02-14T15:11:48Z","_id":"14993","status":"public","quality_controlled":"1","day":"02","publication":"1st Workshop on Machine Learning & Global Health","publication_status":"published","main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=jHY_G91R880"}],"oa_version":"Published Version","date_published":"2023-03-02T00:00:00Z","month":"03","acknowledgement":"Houcemeddine Turki’s contributions to this final output have been funded through the Adapting\r\nWikidata to support clinical practice using Data Science, Semantic Web and Machine Learning\r\nproject, which is part of the Wikimedia Research Fund maintained by the Wikimedia Foundation in San Francisco, California, United States of America.","publisher":"OpenReview","date_updated":"2024-02-28T12:12:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Currin, Christopher","last_name":"Currin","orcid":"0000-0002-4809-5059","id":"e8321fc5-3091-11eb-8a53-83f309a11ac9","first_name":"Christopher"},{"last_name":"Asiedu ","first_name":"Mercy Nyamewaa","full_name":"Asiedu , Mercy Nyamewaa"},{"full_name":"Fourie, Chris","last_name":"Fourie","first_name":"Chris"},{"last_name":"Rosman","first_name":"Benjamin","full_name":"Rosman, Benjamin"},{"full_name":"Turki, Houcemeddine","first_name":"Houcemeddine","last_name":"Turki"},{"full_name":"Lambebo Tonja, Atnafu","first_name":"Atnafu","last_name":"Lambebo Tonja"},{"first_name":"Jade","last_name":"Abbott","full_name":"Abbott, Jade"},{"last_name":"Ajala","first_name":"Marvellous","full_name":"Ajala, Marvellous"},{"last_name":"Adedayo","first_name":"Sadiq Adewale","full_name":"Adedayo, Sadiq Adewale"},{"first_name":"Chris Chinenye","last_name":"Emezue","full_name":"Emezue, Chris Chinenye"},{"full_name":"Machangara, Daphne","first_name":"Daphne","last_name":"Machangara"}],"oa":1,"title":"A framework for grassroots research collaboration in machine learning and global health","article_processing_charge":"No","conference":{"end_date":"2023-05-05","start_date":"2023-05-05","name":"ICLR: International Conference on Learning Representations","location":"Kigali, Rwanda"},"department":[{"_id":"TiVo"}],"language":[{"iso":"eng"}],"year":"2023"},{"doi":"10.5281/ZENODO.7877790","main_file_link":[{"url":"https://doi.org/10.5281/zenodo.7877790","open_access":"1"}],"oa_version":"Published Version","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"14758"}]},"abstract":[{"lang":"eng","text":"This resource contains the artifacts for reproducing the experimental results presented in the paper titled \"A Flexible Toolchain for Symbolic Rabin Games under Fair and Stochastic Uncertainties\" that has been submitted in CAV 2023."}],"type":"research_data_reference","citation":{"apa":"Majumdar, R., Mallik, K., Rychlicki, M., Schmuck, A.-K., &#38; Soudjani, S. (2023). A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.7877790\">https://doi.org/10.5281/ZENODO.7877790</a>","mla":"Majumdar, Rupak, et al. <i>A Flexible Toolchain for Symbolic Rabin Games under Fair and Stochastic Uncertainties</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.7877790\">10.5281/ZENODO.7877790</a>.","ieee":"R. Majumdar, K. Mallik, M. Rychlicki, A.-K. Schmuck, and S. Soudjani, “A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties.” Zenodo, 2023.","short":"R. Majumdar, K. Mallik, M. Rychlicki, A.-K. Schmuck, S. Soudjani, (2023).","ista":"Majumdar R, Mallik K, Rychlicki M, Schmuck A-K, Soudjani S. 2023. A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.7877790\">10.5281/ZENODO.7877790</a>.","ama":"Majumdar R, Mallik K, Rychlicki M, Schmuck A-K, Soudjani S. A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.7877790\">10.5281/ZENODO.7877790</a>","chicago":"Majumdar, Rupak, Kaushik Mallik, Mateusz Rychlicki, Anne-Kathrin Schmuck, and Sadegh Soudjani. “A Flexible Toolchain for Symbolic Rabin Games under Fair and Stochastic Uncertainties.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.7877790\">https://doi.org/10.5281/ZENODO.7877790</a>."},"status":"public","day":"28","_id":"14994","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)"},"ddc":["000"],"date_created":"2024-02-14T15:13:00Z","department":[{"_id":"ToHe"}],"article_processing_charge":"No","year":"2023","publisher":"Zenodo","date_published":"2023-04-28T00:00:00Z","has_accepted_license":"1","month":"04","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties","author":[{"full_name":"Majumdar, Rupak","first_name":"Rupak","last_name":"Majumdar"},{"id":"0834ff3c-6d72-11ec-94e0-b5b0a4fb8598","first_name":"Kaushik","orcid":"0000-0001-9864-7475","last_name":"Mallik","full_name":"Mallik, Kaushik"},{"full_name":"Rychlicki, Mateusz","last_name":"Rychlicki","first_name":"Mateusz"},{"full_name":"Schmuck, Anne-Kathrin","last_name":"Schmuck","first_name":"Anne-Kathrin"},{"full_name":"Soudjani, Sadegh","last_name":"Soudjani","first_name":"Sadegh"}],"date_updated":"2024-02-27T07:39:51Z"},{"oa":1,"author":[{"full_name":"Koval, Nikita","id":"2F4DB10C-F248-11E8-B48F-1D18A9856A87","first_name":"Nikita","last_name":"Koval"},{"last_name":"Fedorov","id":"2e711909-896a-11ed-bdf8-eb0f5a2984c6","first_name":"Alexander","full_name":"Fedorov, Alexander"},{"full_name":"Sokolova, Maria","last_name":"Sokolova","first_name":"Maria"},{"first_name":"Dmitry","last_name":"Tsitelov","full_name":"Tsitelov, Dmitry"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Lincheck: A practical framework for testing concurrent data structures on JVM","date_updated":"2024-02-27T07:46:52Z","publisher":"Zenodo","month":"04","date_published":"2023-04-28T00:00:00Z","year":"2023","department":[{"_id":"DaAl"}],"article_processing_charge":"No","day":"28","status":"public","ddc":["000"],"_id":"14995","date_created":"2024-02-14T15:14:13Z","type":"research_data_reference","abstract":[{"lang":"eng","text":"Lincheck is a new practical and user-friendly framework for testing concurrent data structures on the Java Virtual Machine (JVM). It provides a simple and declarative way to write concurrent tests. Instead of describing how to perform the test, users specify what to test by declaring all the operations to examine; the framework automatically handles the rest. As a result, tests written with Lincheck are concise and easy to understand. \r\nThe artifact presents a collection of Lincheck tests that discover new bugs in popular libraries and implementations from the concurrency literature -- they are listed in Table 1, Section 3. To evaluate the performance of Lincheck analysis, the collection of tests also includes those which check correct data structures and, thus, always succeed. Similarly to Table 2, Section 3, the experiments demonstrate the reasonable time to perform a test. Finally, Lincheck provides user-friendly output with an easy-to-follow trace to reproduce a detected error, significantly simplifying further investigation."}],"citation":{"chicago":"Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.7877757\">https://doi.org/10.5281/ZENODO.7877757</a>.","ama":"Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical framework for testing concurrent data structures on JVM. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.7877757\">10.5281/ZENODO.7877757</a>","ista":"Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. 2023. Lincheck: A practical framework for testing concurrent data structures on JVM, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.7877757\">10.5281/ZENODO.7877757</a>.","ieee":"N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM.” Zenodo, 2023.","short":"N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, D.-A. Alistarh, (2023).","mla":"Koval, Nikita, et al. <i>Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.7877757\">10.5281/ZENODO.7877757</a>.","apa":"Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &#38; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.7877757\">https://doi.org/10.5281/ZENODO.7877757</a>"},"oa_version":"Published Version","related_material":{"record":[{"id":"14260","status":"public","relation":"used_in_publication"}]},"main_file_link":[{"url":"https://doi.org/10.5281/zenodo.7877757","open_access":"1"}],"doi":"10.5281/ZENODO.7877757"},{"month":"12","date_published":"2023-12-15T00:00:00Z","acknowledgement":"This work was supported in part by the ERC-2020-AdG 101020093 (VAMOS) and the ERC-2020-\r\nCoG 863818 (FoRM-SMArt).","date_updated":"2025-07-14T09:10:04Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Zikelic, Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","first_name":"Dorde","orcid":"0000-0002-4681-1699","last_name":"Zikelic"},{"last_name":"Lechner","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias","full_name":"Lechner, Mathias"},{"id":"a235593c-d7fa-11eb-a0c5-b22ca3c66ee6","first_name":"Abhinav","last_name":"Verma","full_name":"Verma, Abhinav"},{"full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu","last_name":"Chatterjee","orcid":"0000-0002-4561-241X"},{"first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","last_name":"Henzinger","full_name":"Henzinger, Thomas A"}],"oa":1,"title":"Compositional policy learning in stochastic control systems with formal guarantees","article_processing_charge":"No","external_id":{"arxiv":["2312.01456"]},"conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2023-12-10","end_date":"2023-12-16","location":"New Orleans, LO, United States"},"department":[{"_id":"ToHe"},{"_id":"KrCh"}],"language":[{"iso":"eng"}],"year":"2023","citation":{"mla":"Zikelic, Dorde, et al. “Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees.” <i>37th Conference on Neural Information Processing Systems</i>, 2023.","apa":"Zikelic, D., Lechner, M., Verma, A., Chatterjee, K., &#38; Henzinger, T. A. (2023). Compositional policy learning in stochastic control systems with formal guarantees. In <i>37th Conference on Neural Information Processing Systems</i>. New Orleans, LO, United States.","chicago":"Zikelic, Dorde, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, and Thomas A Henzinger. “Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees.” In <i>37th Conference on Neural Information Processing Systems</i>, 2023.","ieee":"D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, and T. A. Henzinger, “Compositional policy learning in stochastic control systems with formal guarantees,” in <i>37th Conference on Neural Information Processing Systems</i>, New Orleans, LO, United States, 2023.","short":"D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, T.A. Henzinger, in:, 37th Conference on Neural Information Processing Systems, 2023.","ista":"Zikelic D, Lechner M, Verma A, Chatterjee K, Henzinger TA. 2023. Compositional policy learning in stochastic control systems with formal guarantees. 37th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.","ama":"Zikelic D, Lechner M, Verma A, Chatterjee K, Henzinger TA. Compositional policy learning in stochastic control systems with formal guarantees. In: <i>37th Conference on Neural Information Processing Systems</i>. ; 2023."},"type":"conference","abstract":[{"lang":"eng","text":"Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability. Unlike prior work on verifiable RL, our approach leverages the compositional nature of logical specifications provided in SpectRL, to learn over graphs of probabilistic reach-avoid specifications. The formal guarantees are provided by learning neural network policies together with reach-avoid supermartingales (RASM) for the graph’s sub-tasks and then composing them into a global policy. We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies. We implement a prototype of our approach and evaluate it on a Stochastic Nine Rooms environment."}],"arxiv":1,"date_created":"2024-02-25T09:23:24Z","_id":"15023","day":"15","quality_controlled":"1","status":"public","project":[{"name":"Formal Methods for Stochastic Models: Algorithms and Applications","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","call_identifier":"H2020","grant_number":"863818"},{"name":"Vigilant Algorithmic Monitoring of Software","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","grant_number":"101020093","call_identifier":"H2020"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2312.01456"}],"publication_status":"epub_ahead","publication":"37th Conference on Neural Information Processing Systems","ec_funded":1,"oa_version":"Preprint"}]