[{"has_accepted_license":"1","publication_identifier":{"issnl":["1234-4321"]},"publication_status":"published","oa":1,"quality_controlled":"1","ddc":["000"],"date_published":"2024-01-03T00:00:00Z","status":"public","external_id":{"arxiv":["2304.01430"]},"language":[{"iso":"eng"}],"conference":{"start_date":"2024-01-03","location":"Hong Kong, China","name":"CPAL: Conference on Parsimony and Learning","end_date":"2024-01-03"},"month":"01","oa_version":"Published Version","type":"conference","abstract":[{"lang":"eng","text":"We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision. It consists of an adversarial conditional encoder-decoder architecture based on Slot Attention, modified to use the image as context to decode optical flow without attempting to reconstruct the image itself. In the resulting multi-modal representation, one modality (flow) feeds the encoder to produce separate latent codes (slots), whereas the other modality (image) conditions the decoder to generate the first (flow) from the slots. This design frees the representation from having to encode complex nuisance variability in the image due to, for instance, illumination and reflectance properties of the scene. Since customary autoencoding based on minimizing the reconstruction error does not preclude the entire flow from being encoded into a single slot, we modify the loss to an adversarial criterion based on Contextual Information Separation. The resulting min-max optimization fosters the separation of objects and their assignment to different attention slots, leading to Divided Attention, or DivA. DivA outperforms recent unsupervised multi-object motion segmentation methods while tripling run-time speed up to 104FPS and reducing the performance gap from supervised methods to 12% or less. DivA can handle different numbers of objects and different image sizes at training and test time, is invariant to permutation of object labels, and does not require explicit regularization."}],"file":[{"access_level":"open_access","date_created":"2024-02-12T08:40:36Z","checksum":"8fad894c34f1b3d5a14fb8ffb12f7277","date_updated":"2024-02-12T08:40:36Z","file_id":"14978","creator":"dernst","content_type":"application/pdf","relation":"main_file","file_size":8038511,"success":1,"file_name":"2024_CPAL_Lao.pdf"}],"day":"03","date_updated":"2025-02-13T08:10:28Z","author":[{"full_name":"Lao, Dong","first_name":"Dong","last_name":"Lao"},{"last_name":"Hu","first_name":"Zhengyang","full_name":"Hu, Zhengyang"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"},{"first_name":"Yanchao","last_name":"Yang","full_name":"Yang, Yanchao"},{"last_name":"Soatto","first_name":"Stefano","full_name":"Soatto, Stefano"}],"date_created":"2023-08-22T14:19:59Z","file_date_updated":"2024-02-12T08:40:36Z","arxiv":1,"title":"Divided attention: Unsupervised multi-object discovery with contextually separated slots","year":"2024","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","_id":"14213","publication":"1st Conference on Parsimony and Learning"},{"publication":"arXiv","_id":"14333","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrLo"}],"year":"2023","arxiv":1,"title":"Self-compatibility: Evaluating causal discovery without ground truth","article_number":"2307.09552","date_created":"2023-09-13T12:44:59Z","author":[{"full_name":"Faller, Philipp M.","first_name":"Philipp M.","last_name":"Faller"},{"last_name":"Vankadara","first_name":"Leena Chennuru","full_name":"Vankadara, Leena Chennuru"},{"first_name":"Atalanti A.","last_name":"Mastakouri","full_name":"Mastakouri, Atalanti A."},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Janzing, Dominik","first_name":"Dominik","last_name":"Janzing"}],"abstract":[{"text":"As causal ground truth is incredibly rare, causal discovery algorithms are\r\ncommonly only evaluated on simulated data. This is concerning, given that\r\nsimulations reflect common preconceptions about generating processes regarding\r\nnoise distributions, model classes, and more. In this work, we propose a novel\r\nmethod for falsifying the output of a causal discovery algorithm in the absence\r\nof ground truth. Our key insight is that while statistical learning seeks\r\nstability across subsets of data points, causal learning should seek stability\r\nacross subsets of variables. Motivated by this insight, our method relies on a\r\nnotion of compatibility between causal graphs learned on different subsets of\r\nvariables. We prove that detecting incompatibilities can falsify wrongly\r\ninferred causal relations due to violation of assumptions or errors from finite\r\nsample effects. Although passing such compatibility tests is only a necessary\r\ncriterion for good performance, we argue that it provides strong evidence for\r\nthe causal models whenever compatibility entails strong implications for the\r\njoint distribution. We also demonstrate experimentally that detection of\r\nincompatibilities can aid in causal model selection.","lang":"eng"}],"day":"18","date_updated":"2023-09-13T12:47:53Z","oa_version":"Preprint","type":"preprint","month":"07","extern":"1","citation":{"mla":"Faller, Philipp M., et al. “Self-Compatibility: Evaluating Causal Discovery without Ground Truth.” <i>ArXiv</i>, 2307.09552, doi:<a href=\"https://doi.org/10.48550/arXiv.2307.09552\">10.48550/arXiv.2307.09552</a>.","ista":"Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility: Evaluating causal discovery without ground truth. arXiv, 2307.09552.","apa":"Faller, P. M., Vankadara, L. C., Mastakouri, A. A., Locatello, F., &#38; Janzing, D. (n.d.). Self-compatibility: Evaluating causal discovery without ground truth. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2307.09552\">https://doi.org/10.48550/arXiv.2307.09552</a>","ama":"Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility: Evaluating causal discovery without ground truth. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2307.09552\">10.48550/arXiv.2307.09552</a>","short":"P.M. Faller, L.C. Vankadara, A.A. Mastakouri, F. Locatello, D. Janzing, ArXiv (n.d.).","ieee":"P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, and D. Janzing, “Self-compatibility: Evaluating causal discovery without ground truth,” <i>arXiv</i>. .","chicago":"Faller, Philipp M., Leena Chennuru Vankadara, Atalanti A. Mastakouri, Francesco Locatello, and Dominik Janzing. “Self-Compatibility: Evaluating Causal Discovery without Ground Truth.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2307.09552\">https://doi.org/10.48550/arXiv.2307.09552</a>."},"language":[{"iso":"eng"}],"external_id":{"arxiv":["2307.09552"]},"status":"public","doi":"10.48550/arXiv.2307.09552","date_published":"2023-07-18T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2307.09552","open_access":"1"}],"publication_status":"submitted","oa":1},{"year":"2023","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"This work was initiated at the Second Bellairs Workshop on Causality held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop participants for providing a stimulating research environment. Further, we thank Cian Eastwood, Luigi Gresele, Stefano Soatto, Marco Bagatella, and A. René Geist for helpful discussion. GM is a member of the Machine Learning Cluster of Excellence, EXC number 2064/1 – Project number 390727645. JvK and GM acknowledge support from the German Federal Ministry of Education and Research (BMBF) through the Tübingen AI Center (FKZ: 01IS18039B). The research of DX and SM was supported by the Air Force Office of Scientific Research under award number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations expressed in\r\nthis material are those of the author(s) and do not necessarily reflect the views of the United States Air Force. We also thank SURF for the support in using the Dutch National Supercomputer Snellius. DY was supported by an Amazon fellowship and the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Work done outside of Amazon. SL was supported by an IVADO excellence PhD scholarship and by Samsung Electronics Co., Ldt.","article_processing_charge":"No","publication":"arXiv","_id":"14946","date_updated":"2024-02-12T08:07:33Z","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."}],"day":"07","month":"11","oa_version":"Preprint","type":"preprint","author":[{"first_name":"Dingling","last_name":"Yao","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling"},{"last_name":"Xu","first_name":"Danru","full_name":"Xu, Danru"},{"full_name":"Lachapelle, Sébastien","last_name":"Lachapelle","first_name":"Sébastien"},{"last_name":"Magliacane","first_name":"Sara","full_name":"Magliacane, Sara"},{"full_name":"Taslakian, Perouz","first_name":"Perouz","last_name":"Taslakian"},{"full_name":"Martius, Georg","first_name":"Georg","last_name":"Martius"},{"first_name":"Julius von","last_name":"Kügelgen","full_name":"Kügelgen, Julius von"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"}],"date_created":"2024-02-07T14:28:34Z","title":"Multi-view causal representation learning with partial observability","arxiv":1,"article_number":"2311.04056","external_id":{"arxiv":["2311.04056"]},"status":"public","language":[{"iso":"eng"}],"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.","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>.","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>","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>","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>. .","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>."},"oa":1,"publication_status":"submitted","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2311.04056"}],"doi":"10.48550/arXiv.2311.04056","date_published":"2023-11-07T00:00:00Z"},{"oa":1,"publication_status":"submitted","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2307.09437"}],"doi":"10.48550/arXiv.2307.09437","date_published":"2023-07-18T00:00:00Z","status":"public","external_id":{"arxiv":["2307.09437"]},"citation":{"short":"A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).","chicago":"Kori, Avinash, Francesco Locatello, Fabio De Sousa Ribeiro, Francesca Toni, and Ben Glocker. “Grounded Object Centric Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2307.09437\">https://doi.org/10.48550/arXiv.2307.09437</a>.","ieee":"A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded object centric learning,” <i>arXiv</i>. .","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>","ista":"Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. arXiv, 2307.09437.","mla":"Kori, Avinash, et al. “Grounded Object Centric Learning.” <i>ArXiv</i>, 2307.09437, doi:<a href=\"https://doi.org/10.48550/arXiv.2307.09437\">10.48550/arXiv.2307.09437</a>.","ama":"Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2307.09437\">10.48550/arXiv.2307.09437</a>"},"language":[{"iso":"eng"}],"month":"07","type":"preprint","oa_version":"Preprint","day":"18","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"}],"date_updated":"2024-02-12T08:13:12Z","author":[{"full_name":"Kori, Avinash","last_name":"Kori","first_name":"Avinash"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Ribeiro, Fabio De Sousa","last_name":"Ribeiro","first_name":"Fabio De Sousa"},{"first_name":"Francesca","last_name":"Toni","full_name":"Toni, Francesca"},{"last_name":"Glocker","first_name":"Ben","full_name":"Glocker, Ben"}],"date_created":"2024-02-07T14:47:04Z","article_number":"2307.09437","arxiv":1,"title":"Grounded object centric learning","year":"2023","department":[{"_id":"FrLo"}],"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.","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","_id":"14948","publication":"arXiv"},{"publication":"Journal of Machine Learning Research","article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_type":"original","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ML Research Press","department":[{"_id":"FrLo"}],"title":"Image retrieval outperforms diffusion models on data augmentation","author":[{"first_name":"Max","last_name":"Burg","full_name":"Burg, Max"},{"last_name":"Wenzel","first_name":"Florian","full_name":"Wenzel, Florian"},{"first_name":"Dominik","last_name":"Zietlow","full_name":"Zietlow, Dominik"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"full_name":"Makansi, Osama","first_name":"Osama","last_name":"Makansi"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"}],"day":"10","file":[{"date_created":"2024-02-07T14:57:32Z","access_level":"open_access","file_id":"14950","date_updated":"2024-02-07T14:57:32Z","checksum":"af87ddea7908923426365347b9c87ba7","file_size":27325153,"relation":"main_file","content_type":"application/pdf","creator":"ptazenko","file_name":"Burg_et_al_2023_Image_retrieval_outperforms.pdf"}],"language":[{"iso":"eng"}],"quality_controlled":"1","publication_identifier":{"eissn":["2835-8856"]},"_id":"14949","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).","year":"2023","date_created":"2024-02-07T14:57:39Z","file_date_updated":"2024-02-07T14:57:32Z","abstract":[{"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.","lang":"eng"}],"date_updated":"2024-02-12T08:30:21Z","month":"12","oa_version":"Published Version","type":"journal_article","citation":{"ieee":"M. Burg <i>et al.</i>, “Image retrieval outperforms diffusion models on data augmentation,” <i>Journal of Machine Learning Research</i>. ML Research Press, 2023.","chicago":"Burg, Max, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” <i>Journal of Machine Learning Research</i>. ML Research Press, 2023.","short":"M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, Journal of Machine Learning Research (2023).","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.","mla":"Burg, Max, et al. “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.","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."},"alternative_title":["TMLR"],"status":"public","ddc":["000"],"date_published":"2023-12-10T00:00:00Z","main_file_link":[{"url":"https://openreview.net/forum?id=xflYdGZMpv","open_access":"1"}],"publication_status":"published","oa":1,"has_accepted_license":"1"},{"article_processing_charge":"No","publication":"arXiv","_id":"14952","year":"2023","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","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.","date_created":"2024-02-07T15:08:55Z","title":"Latent space translation via semantic alignment","arxiv":1,"article_number":"2311.00664","date_updated":"2024-02-12T09:40:23Z","abstract":[{"text":"While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible. Towards a better understanding of this phenomenon, our work shows how representations learned from these neural modules can be translated between different pre-trained networks via simpler transformations than previously thought. An advantage of this approach is the ability to\r\nestimate these transformations using standard, well-understood algebraic procedures that have closed-form solutions. Our method directly estimates a transformation between two given latent spaces, thereby enabling effective stitching of encoders and decoders without additional training. We extensively validate the adaptability of this translation procedure in different\r\nexperimental settings: across various trainings, domains, architectures (e.g., ResNet, CNN, ViT), and in multiple downstream tasks (classification, reconstruction). Notably, we show how it is possible to zero-shot stitch text encoders and vision decoders, or vice-versa, yielding surprisingly good classification performance in this multimodal setting.","lang":"eng"}],"day":"01","type":"preprint","oa_version":"Preprint","month":"11","author":[{"full_name":"Maiorca, Valentino","last_name":"Maiorca","first_name":"Valentino"},{"full_name":"Moschella, Luca","first_name":"Luca","last_name":"Moschella"},{"first_name":"Antonio","last_name":"Norelli","full_name":"Norelli, Antonio"},{"last_name":"Fumero","first_name":"Marco","full_name":"Fumero, Marco"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Rodolà, Emanuele","first_name":"Emanuele","last_name":"Rodolà"}],"citation":{"ama":"Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2311.00664\">10.48550/arXiv.2311.00664</a>","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>.","ista":"Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. arXiv, 2311.00664.","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>.","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.)."},"language":[{"iso":"eng"}],"status":"public","external_id":{"arxiv":["2311.00664"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2311.00664"}],"doi":"10.48550/arXiv.2311.00664","date_published":"2023-11-01T00:00:00Z","oa":1,"publication_status":"submitted"},{"article_number":"2310.18123","title":"Sample complexity bounds for score-matching: Causal discovery and generative modeling","arxiv":1,"date_created":"2024-02-07T15:11:11Z","author":[{"first_name":"Zhenyu","last_name":"Zhu","full_name":"Zhu, Zhenyu"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco"},{"last_name":"Cevher","first_name":"Volkan","full_name":"Cevher, Volkan"}],"month":"10","type":"preprint","oa_version":"Preprint","day":"27","abstract":[{"lang":"eng","text":"This paper provides statistical sample complexity bounds for score-matching and its applications in causal discovery. We demonstrate that accurate estimation of the score function is achievable by training a standard deep ReLU neural network using stochastic gradient descent. We establish bounds on the error rate of recovering causal relationships using the score-matching-based causal discovery method of Rolland et al. [2022], assuming a sufficiently good estimation of the score function. Finally, we analyze the upper bound of score-matching estimation within the score-based generative modeling, which has been applied for causal discovery but is also of independent interest within the domain of generative models."}],"date_updated":"2024-02-12T09:45:58Z","_id":"14953","publication":"arXiv","article_processing_charge":"No","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. ","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrLo"}],"year":"2023","doi":"10.48550/arXiv.2310.18123","date_published":"2023-10-27T00:00:00Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2310.18123"}],"oa":1,"publication_status":"submitted","language":[{"iso":"eng"}],"citation":{"ista":"Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv, 2310.18123.","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>","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>","short":"Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).","ieee":"Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” <i>arXiv</i>. .","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>."},"external_id":{"arxiv":["2310.18123"]},"status":"public"},{"oa":1,"publication_status":"submitted","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.13387","open_access":"1"}],"date_published":"2023-10-20T00:00:00Z","doi":"10.48550/arXiv.2310.13387","status":"public","external_id":{"arxiv":["2310.13387"]},"language":[{"iso":"eng"}],"citation":{"short":"F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello, ArXiv (n.d.).","ieee":"F. Montagna <i>et al.</i>, “Assumption violations in causal discovery and the robustness of score matching,” <i>arXiv</i>. .","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>.","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.","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>.","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>","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>"},"day":"20","abstract":[{"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.","lang":"eng"}],"date_updated":"2024-02-12T09:51:15Z","type":"preprint","oa_version":"Preprint","month":"10","author":[{"full_name":"Montagna, Francesco","last_name":"Montagna","first_name":"Francesco"},{"full_name":"Mastakouri, Atalanti A.","first_name":"Atalanti A.","last_name":"Mastakouri"},{"first_name":"Elias","last_name":"Eulig","full_name":"Eulig, Elias"},{"full_name":"Noceti, Nicoletta","first_name":"Nicoletta","last_name":"Noceti"},{"first_name":"Lorenzo","last_name":"Rosasco","full_name":"Rosasco, Lorenzo"},{"full_name":"Janzing, Dominik","last_name":"Janzing","first_name":"Dominik"},{"first_name":"Bryon","last_name":"Aragam","full_name":"Aragam, Bryon"},{"last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"date_created":"2024-02-07T15:11:56Z","title":"Assumption violations in causal discovery and the robustness of score matching","arxiv":1,"article_number":"2310.13387","year":"2023","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","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_processing_charge":"No","publication":"arXiv","_id":"14954"},{"has_accepted_license":"1","oa":1,"publication_status":"published","main_file_link":[{"url":"https://openreview.net/forum?id=Whr6uobelR","open_access":"1"}],"ddc":["000"],"date_published":"2023-12-05T00:00:00Z","status":"public","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.","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.","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.","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.","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.","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.","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."},"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."}],"date_updated":"2024-02-13T08:59:27Z","month":"12","oa_version":"Published Version","type":"conference","file_date_updated":"2024-02-13T08:50:53Z","date_created":"2024-02-07T15:17:51Z","year":"2023","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","_id":"14958","quality_controlled":"1","language":[{"iso":"eng"}],"conference":{"end_date":"2023-12-15","start_date":"2023-12-15","name":"CRL: Causal Representation Learning Workshop at NeurIPS","location":"New Orleans, LA, United States"},"day":"05","file":[{"access_level":"open_access","date_created":"2024-02-13T08:50:53Z","checksum":"484efc27bda75ed6666044989695d9b6","date_updated":"2024-02-13T08:50:53Z","file_id":"14982","creator":"dernst","relation":"main_file","content_type":"application/pdf","file_size":552357,"success":1,"file_name":"2023_CRL_Xu.pdf"}],"author":[{"first_name":"Danru","last_name":"Xu","full_name":"Xu, Danru"},{"id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling","first_name":"Dingling","last_name":"Yao"},{"last_name":"Lachapelle","first_name":"Sebastien","full_name":"Lachapelle, Sebastien"},{"last_name":"Taslakian","first_name":"Perouz","full_name":"Taslakian, Perouz"},{"last_name":"von Kügelgen","first_name":"Julius","full_name":"von Kügelgen, Julius"},{"last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"},{"full_name":"Magliacane, Sara","last_name":"Magliacane","first_name":"Sara"}],"title":"A sparsity principle for partially observable causal representation learning","article_number":"54","department":[{"_id":"FrLo"}],"publisher":"OpenReview","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"publication":"Causal Representation Learning Workshop at NeurIPS 2023"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrLo"}],"year":"2023","_id":"14961","publication":"arXiv","article_processing_charge":"No","author":[{"full_name":"Montagna, Francesco","first_name":"Francesco","last_name":"Montagna"},{"last_name":"Noceti","first_name":"Nicoletta","full_name":"Noceti, Nicoletta"},{"full_name":"Rosasco, Lorenzo","first_name":"Lorenzo","last_name":"Rosasco"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"}],"month":"10","type":"preprint","oa_version":"Preprint","date_updated":"2024-02-12T10:03:33Z","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"}],"day":"22","article_number":"2310.14246","arxiv":1,"title":"Shortcuts for causal discovery of nonlinear models by score matching","date_created":"2024-02-08T15:31:46Z","status":"public","external_id":{"arxiv":["2310.14246"]},"citation":{"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>.","ieee":"F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal discovery of nonlinear models by score matching,” <i>arXiv</i>. .","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>.","ista":"Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. arXiv, 2310.14246.","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>"},"language":[{"iso":"eng"}],"publication_status":"submitted","oa":1,"date_published":"2023-10-22T00:00:00Z","doi":"10.48550/arXiv.2310.14246","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.14246","open_access":"1"}]},{"publication":"arXiv","_id":"14962","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","department":[{"_id":"FrLo"}],"arxiv":1,"title":"Unsupervised open-vocabulary object localization in videos","article_number":"2309.09858","date_created":"2024-02-08T15:33:39Z","author":[{"full_name":"Fan, Ke","first_name":"Ke","last_name":"Fan"},{"full_name":"Bai, Zechen","first_name":"Zechen","last_name":"Bai"},{"last_name":"Xiao","first_name":"Tianjun","full_name":"Xiao, Tianjun"},{"last_name":"Zietlow","first_name":"Dominik","full_name":"Zietlow, Dominik"},{"last_name":"Horn","first_name":"Max","full_name":"Horn, Max"},{"full_name":"Zhao, Zixu","last_name":"Zhao","first_name":"Zixu"},{"last_name":"Carl-Johann Simon-Gabriel","first_name":"Carl-Johann Simon-Gabriel","full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel"},{"full_name":"Shou, Mike Zheng","last_name":"Shou","first_name":"Mike Zheng"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Schiele, Bernt","last_name":"Schiele","first_name":"Bernt"},{"full_name":"Brox, Thomas","last_name":"Brox","first_name":"Thomas"},{"last_name":"Zhang","first_name":"Zheng","full_name":"Zhang, Zheng"},{"first_name":"Yanwei","last_name":"Fu","full_name":"Fu, Yanwei"},{"first_name":"Tong","last_name":"He","full_name":"He, Tong"}],"day":"18","date_updated":"2024-02-12T10:12:22Z","abstract":[{"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.","lang":"eng"}],"type":"preprint","oa_version":"Preprint","month":"09","extern":"1","language":[{"iso":"eng"}],"citation":{"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>.","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.).","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.","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>.","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>"},"status":"public","external_id":{"arxiv":["2309.09858"]},"date_published":"2023-09-18T00:00:00Z","doi":"10.48550/arXiv.2309.09858","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2309.09858","open_access":"1"}],"oa":1,"publication_status":"submitted"},{"oa":1,"publication_status":"submitted","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2309.00233","open_access":"1"}],"date_published":"2023-09-01T00:00:00Z","doi":"10.48550/arXiv.2309.00233","external_id":{"arxiv":["2309.00233"]},"status":"public","language":[{"iso":"eng"}],"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.","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>.","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>","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>","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.).","ieee":"Z. Zhao <i>et al.</i>, “Object-centric multiple object tracking,” <i>arXiv</i>. .","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>."},"extern":"1","type":"preprint","oa_version":"Preprint","month":"09","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"}],"day":"01","date_updated":"2024-02-12T10:16:21Z","author":[{"full_name":"Zhao, Zixu","last_name":"Zhao","first_name":"Zixu"},{"full_name":"Wang, Jiaze","first_name":"Jiaze","last_name":"Wang"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"full_name":"Ding, Yizhuo","first_name":"Yizhuo","last_name":"Ding"},{"first_name":"Tong","last_name":"He","full_name":"He, Tong"},{"full_name":"Bai, Zechen","first_name":"Zechen","last_name":"Bai"},{"last_name":"Zietlow","first_name":"Dominik","full_name":"Zietlow, Dominik"},{"first_name":"Carl-Johann Simon-Gabriel","last_name":"Carl-Johann Simon-Gabriel","full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel"},{"full_name":"Shuai, Bing","first_name":"Bing","last_name":"Shuai"},{"full_name":"Tu, Zhuowen","last_name":"Tu","first_name":"Zhuowen"},{"full_name":"Brox, Thomas","last_name":"Brox","first_name":"Thomas"},{"last_name":"Schiele","first_name":"Bernt","full_name":"Schiele, Bernt"},{"full_name":"Fu, Yanwei","first_name":"Yanwei","last_name":"Fu"},{"last_name":"Locatello","first_name":"Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"},{"full_name":"Zhang, Zheng","first_name":"Zheng","last_name":"Zhang"},{"first_name":"Tianjun","last_name":"Xiao","full_name":"Xiao, Tianjun"}],"date_created":"2024-02-08T15:34:43Z","article_number":"2309.00233","title":"Object-centric multiple object tracking","arxiv":1,"department":[{"_id":"FrLo"}],"year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","_id":"14963","publication":"arXiv"},{"language":[{"iso":"eng"}],"conference":{"end_date":"2023-01-07","location":"Waikoloa, HI, United States","name":"WACV: Winter Conference on Applications of Computer Vision","start_date":"2023-01-02"},"quality_controlled":"1","doi":"10.1109/wacv56688.2023.00278","publication_identifier":{"isbn":["9781665493475"],"eissn":["2642-9381"]},"article_processing_charge":"No","scopus_import":"1","publication":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision","department":[{"_id":"FrLo"}],"publisher":"Institute of Electrical and Electronics Engineers","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"TeST: Test-time Self-Training under distribution shift","arxiv":1,"day":"06","author":[{"first_name":"Samarth","last_name":"Sinha","full_name":"Sinha, Samarth"},{"first_name":"Peter","last_name":"Gehler","full_name":"Gehler, Peter"},{"first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"},{"full_name":"Schiele, Bernt","first_name":"Bernt","last_name":"Schiele"}],"citation":{"ama":"Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under distribution shift. In: <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Institute of Electrical and Electronics Engineers; 2023. doi:<a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">10.1109/wacv56688.2023.00278</a>","apa":"Sinha, S., Gehler, P., Locatello, F., &#38; Schiele, B. (2023). TeST: Test-time Self-Training under distribution shift. In <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Waikoloa, HI, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">https://doi.org/10.1109/wacv56688.2023.00278</a>","ista":"Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision.","mla":"Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.” <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>, Institute of Electrical and Electronics Engineers, 2023, doi:<a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">10.1109/wacv56688.2023.00278</a>.","chicago":"Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele. “TeST: Test-Time Self-Training under Distribution Shift.” In <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Institute of Electrical and Electronics Engineers, 2023. <a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">https://doi.org/10.1109/wacv56688.2023.00278</a>.","ieee":"S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>, Waikoloa, HI, United States, 2023.","short":"S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023."},"extern":"1","status":"public","external_id":{"arxiv":["2209.11459"]},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2209.11459"}],"date_published":"2023-02-06T00:00:00Z","oa":1,"publication_status":"published","_id":"14105","year":"2023","date_created":"2023-08-21T12:11:38Z","date_updated":"2023-09-06T10:26:56Z","abstract":[{"lang":"eng","text":"Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline testtime adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms [4, 43], while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks:\r\nobject detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new stateof-the art for test-time domain adaptation algorithms. "}],"oa_version":"Preprint","month":"02","type":"conference"},{"language":[{"iso":"eng"}],"citation":{"mla":"Löwe, Sindy, et al. “Rotating Features for Object Discovery.” <i>ArXiv</i>, 2306.00600, doi:<a href=\"https://doi.org/10.48550/arXiv.2306.00600\">10.48550/arXiv.2306.00600</a>.","ista":"Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery. arXiv, 2306.00600.","apa":"Löwe, S., Lippe, P., Locatello, F., &#38; Welling, M. (n.d.). Rotating features for object discovery. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2306.00600\">https://doi.org/10.48550/arXiv.2306.00600</a>","ama":"Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2306.00600\">10.48550/arXiv.2306.00600</a>","short":"S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).","ieee":"S. Löwe, P. Lippe, F. Locatello, and M. Welling, “Rotating features for object discovery,” <i>arXiv</i>. .","chicago":"Löwe, Sindy, Phillip Lippe, Francesco Locatello, and Max Welling. “Rotating Features for Object Discovery.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2306.00600\">https://doi.org/10.48550/arXiv.2306.00600</a>."},"external_id":{"arxiv":["2306.00600"]},"status":"public","date_published":"2023-06-01T00:00:00Z","doi":"10.48550/arXiv.2306.00600","main_file_link":[{"url":"https://arxiv.org/abs/2306.00600","open_access":"1"}],"oa":1,"publication_status":"submitted","publication":"arXiv","_id":"14207","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","department":[{"_id":"FrLo"}],"arxiv":1,"title":"Rotating features for object discovery","article_number":"2306.00600","date_created":"2023-08-22T14:18:00Z","author":[{"full_name":"Löwe, Sindy","last_name":"Löwe","first_name":"Sindy"},{"full_name":"Lippe, Phillip","first_name":"Phillip","last_name":"Lippe"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"},{"first_name":"Max","last_name":"Welling","full_name":"Welling, Max"}],"day":"01","abstract":[{"text":"The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate. Most machine learning efforts addressing this issue in an unsupervised setting have focused on slot-based methods, which may be limiting due to their discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder was proposed as an alternative that learns continuous and distributed object-centric representations. However, it is only applicable to simple toy data. In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations. Additionally, we show the applicability of our approach to pre-trained features. Together, these advancements enable us to scale distributed object-centric representations from simple toy to real-world data. We believe this work advances a new paradigm for addressing the binding problem in machine learning and has the potential to inspire further innovation in the field.","lang":"eng"}],"date_updated":"2024-02-12T09:53:44Z","month":"06","type":"preprint","oa_version":"Preprint"},{"date_published":"2023-05-30T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2305.19377","open_access":"1"}],"oa":1,"publication_status":"published","extern":"1","intvolume":"       202","citation":{"short":"Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.","chicago":"Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.” In <i>Proceedings of the 40th International Conference on Machine Learning</i>, 202:43105–28. ML Research Press, 2023.","ieee":"Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in <i>Proceedings of the 40th International Conference on Machine Learning</i>, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128.","apa":"Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., &#38; Cevher, V. (2023). Benign overfitting in deep neural networks under lazy training. In <i>Proceedings of the 40th International Conference on Machine Learning</i> (Vol. 202, pp. 43105–43128). Honolulu, Hawaii, United States: ML Research Press.","ista":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting in deep neural networks under lazy training. Proceedings of the 40th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 202, 43105–43128.","mla":"Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy Training.” <i>Proceedings of the 40th International Conference on Machine Learning</i>, vol. 202, ML Research Press, 2023, pp. 43105–28.","ama":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: <i>Proceedings of the 40th International Conference on Machine Learning</i>. Vol 202. ML Research Press; 2023:43105-43128."},"alternative_title":["PMLR"],"external_id":{"arxiv":["2305.19377"]},"status":"public","volume":202,"date_created":"2023-08-22T14:18:18Z","page":"43105-43128","month":"05","type":"conference","oa_version":"Preprint","date_updated":"2023-09-13T08:46:46Z","abstract":[{"lang":"eng","text":"This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly) zero-training error under the lazy training regime. For this purpose, we unify three interrelated concepts of overparameterization, benign overfitting, and the Lipschitz constant of DNNs. Our results indicate that interpolating with smoother functions leads to better generalization. Furthermore, we investigate the special case where interpolating smooth ground-truth functions is performed by DNNs under the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates that the generalization error converges to a constant order that only depends on label noise and initialization noise, which theoretically verifies benign overfitting. Our analysis provides a tight lower bound on the normalized margin under non-smooth activation functions, as well as the minimum eigenvalue of NTK under high-dimensional settings, which has its own interest in learning theory."}],"_id":"14208","year":"2023","quality_controlled":"1","conference":{"name":"International Conference on Machine Learning","location":"Honolulu, Hawaii, United States","start_date":"2023-07-23","end_date":"2023-07-29"},"language":[{"iso":"eng"}],"title":"Benign overfitting in deep neural networks under lazy training","arxiv":1,"author":[{"full_name":"Zhu, Zhenyu","first_name":"Zhenyu","last_name":"Zhu"},{"last_name":"Liu","first_name":"Fanghui","full_name":"Liu, Fanghui"},{"full_name":"Chrysos, Grigorios G","first_name":"Grigorios G","last_name":"Chrysos"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Cevher, Volkan","first_name":"Volkan","last_name":"Cevher"}],"day":"30","publication":"Proceedings of the 40th International Conference on Machine Learning","article_processing_charge":"No","publisher":"ML Research Press","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrLo"}]},{"day":"20","date_updated":"2023-09-13T08:51:56Z","abstract":[{"text":"Diffusion models excel at generating photorealistic images from text-queries. Naturally, many approaches have been proposed to use these generative abilities to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large noisily supervised, but nonetheless, annotated datasets. It is an open question whether the generalization capabilities of diffusion models beyond using the additional data of the pre-training process for augmentation lead to improved downstream performance. We perform a systematic evaluation of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. While we find that personalizing diffusion models towards the target data outperforms simpler prompting strategies, we also show that using the training data of the diffusion model alone, via a simple nearest neighbor retrieval procedure, leads to even stronger downstream performance. Overall, our study probes the limitations of diffusion models for data augmentation but also highlights its potential in generating new training data to improve performance on simple downstream vision tasks.","lang":"eng"}],"oa_version":"Preprint","type":"preprint","month":"04","author":[{"first_name":"Max F.","last_name":"Burg","full_name":"Burg, Max F."},{"last_name":"Wenzel","first_name":"Florian","full_name":"Wenzel, Florian"},{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"full_name":"Makansi, Osama","first_name":"Osama","last_name":"Makansi"},{"first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Russell, Chris","first_name":"Chris","last_name":"Russell"}],"date_created":"2023-08-22T14:18:43Z","title":"A data augmentation perspective on diffusion models and retrieval","arxiv":1,"article_number":"2304.10253","year":"2023","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","publication":"arXiv","_id":"14209","publication_status":"submitted","oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2304.10253"}],"date_published":"2023-04-20T00:00:00Z","doi":"10.48550/arXiv.2304.10253","status":"public","external_id":{"arxiv":["2304.10253"]},"citation":{"chicago":"Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>.","ieee":"M. F. Burg <i>et al.</i>, “A data augmentation perspective on diffusion models and retrieval,” <i>arXiv</i>. .","short":"M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.).","ama":"Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>","apa":"Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &#38; Russell, C. (n.d.). A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>","mla":"Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, 2304.10253, doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>.","ista":"Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253."},"language":[{"iso":"eng"}],"extern":"1"},{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2304.07939"}],"date_published":"2023-04-17T00:00:00Z","doi":"10.48550/arXiv.2304.07939","publication_status":"submitted","oa":1,"language":[{"iso":"eng"}],"citation":{"chicago":"Fumero, Marco, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2304.07939\">https://doi.org/10.48550/arXiv.2304.07939</a>.","ieee":"M. Fumero <i>et al.</i>, “Leveraging sparse and shared feature activations for disentangled representation learning,” <i>arXiv</i>. .","short":"M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).","ama":"Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature activations for disentangled representation learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2304.07939\">10.48550/arXiv.2304.07939</a>","apa":"Fumero, M., Wenzel, F., Zancato, L., Achille, A., Rodolà, E., Soatto, S., … Locatello, F. (n.d.). Leveraging sparse and shared feature activations for disentangled representation learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2304.07939\">https://doi.org/10.48550/arXiv.2304.07939</a>","ista":"Fumero M, Wenzel F, Zancato L, Achille A, Rodolà E, Soatto S, Schölkopf B, Locatello F. Leveraging sparse and shared feature activations for disentangled representation learning. arXiv, 2304.07939.","mla":"Fumero, Marco, et al. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” <i>ArXiv</i>, 2304.07939, doi:<a href=\"https://doi.org/10.48550/arXiv.2304.07939\">10.48550/arXiv.2304.07939</a>."},"status":"public","external_id":{"arxiv":["2304.07939"]},"date_created":"2023-08-22T14:19:03Z","article_number":"2304.07939","title":"Leveraging sparse and shared feature activations for disentangled representation learning","arxiv":1,"type":"preprint","month":"04","oa_version":"Preprint","day":"17","date_updated":"2024-02-12T09:55:48Z","abstract":[{"lang":"eng","text":"Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. Assuming each supervised task only depends on an unknown subset of the factors of variation, we disentangle the feature space of a supervised multi-task model, with features activating sparsely across different tasks and information being shared as appropriate. Importantly, we never directly observe the factors of variations but establish that access to multiple tasks is sufficient for identifiability under sufficiency and minimality assumptions. We validate our approach on six real world distribution shift benchmarks, and different data modalities (images, text), demonstrating how disentangled representations can be transferred to real settings."}],"author":[{"full_name":"Fumero, Marco","first_name":"Marco","last_name":"Fumero"},{"last_name":"Wenzel","first_name":"Florian","full_name":"Wenzel, Florian"},{"last_name":"Zancato","first_name":"Luca","full_name":"Zancato, Luca"},{"full_name":"Achille, Alessandro","first_name":"Alessandro","last_name":"Achille"},{"full_name":"Rodolà, Emanuele","last_name":"Rodolà","first_name":"Emanuele"},{"full_name":"Soatto, Stefano","last_name":"Soatto","first_name":"Stefano"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"}],"article_processing_charge":"No","_id":"14210","publication":"arXiv","department":[{"_id":"FrLo"}],"year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"author":[{"last_name":"Montagna","first_name":"Francesco","full_name":"Montagna, Francesco"},{"last_name":"Noceti","first_name":"Nicoletta","full_name":"Noceti, Nicoletta"},{"last_name":"Rosasco","first_name":"Lorenzo","full_name":"Rosasco, Lorenzo"},{"full_name":"Zhang, Kun","last_name":"Zhang","first_name":"Kun"},{"first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"}],"date_updated":"2023-09-13T09:00:31Z","abstract":[{"text":"Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian noise assumption on additive non-linear models, which is common to many causal discovery approaches. In this paper we show the shortcomings of inference under this hypothesis, analyzing the risk of edge inversion under violation of Gaussianity of the noise terms. Then, we propose a novel method for inferring the topological ordering of the variables in the causal graph, from data generated according to an additive non-linear model with a generic noise distribution. This leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm with a minimal set of assumptions and state of the art performance, experimentally benchmarked on synthetic data.","lang":"eng"}],"day":"01","oa_version":"Preprint","month":"04","type":"conference","arxiv":1,"title":"Causal discovery with score matching on additive models with arbitrary noise","date_created":"2023-08-22T14:19:21Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","department":[{"_id":"FrLo"}],"publication":"2nd Conference on Causal Learning and Reasoning","_id":"14211","scopus_import":"1","article_processing_charge":"No","oa":1,"publication_status":"published","date_published":"2023-04-01T00:00:00Z","main_file_link":[{"url":"https://arxiv.org/abs/2304.03265","open_access":"1"}],"quality_controlled":"1","status":"public","external_id":{"arxiv":["2304.03265"]},"extern":"1","conference":{"end_date":"2023-04-14","start_date":"2023-04-11","location":"Tübingen, Germany","name":"CLeaR: Conference on Causal Learning and Reasoning"},"language":[{"iso":"eng"}],"citation":{"short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery with score matching on additive models with arbitrary noise. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","mla":"Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Causal discovery with score matching on additive models with arbitrary noise. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023."}},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","department":[{"_id":"FrLo"}],"_id":"14212","publication":"2nd Conference on Causal Learning and Reasoning","article_processing_charge":"No","scopus_import":"1","author":[{"first_name":"Francesco","last_name":"Montagna","full_name":"Montagna, Francesco"},{"full_name":"Noceti, Nicoletta","last_name":"Noceti","first_name":"Nicoletta"},{"first_name":"Lorenzo","last_name":"Rosasco","full_name":"Rosasco, Lorenzo"},{"full_name":"Zhang, Kun","last_name":"Zhang","first_name":"Kun"},{"first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"}],"type":"conference","oa_version":"Preprint","month":"04","day":"01","abstract":[{"lang":"eng","text":"This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function ∇logp(X), we extend the work of Rolland et al. (2022) that only recovers the topological order from the score and requires an expensive pruning step removing spurious edges among those admitted by the ordering. Our analysis leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces the complexity of the pruning by a factor proportional to the graph size. In practice, DAS achieves competitive accuracy with current state-of-the-art while being over an order of magnitude faster. Overall, our approach enables principled and scalable causal discovery, significantly lowering the compute bar."}],"date_updated":"2023-09-13T09:03:24Z","title":"Scalable causal discovery with score matching","arxiv":1,"date_created":"2023-08-22T14:19:40Z","status":"public","external_id":{"arxiv":["2304.03382"]},"conference":{"end_date":"2023-04-14","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning","location":"Tübingen, Germany"},"extern":"1","language":[{"iso":"eng"}],"citation":{"ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","mla":"Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal discovery with score matching. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Scalable causal discovery with score matching. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany."},"publication_status":"published","oa":1,"date_published":"2023-04-01T00:00:00Z","quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/2304.03382","open_access":"1"}]},{"author":[{"full_name":"Liu, Yuejiang","first_name":"Yuejiang","last_name":"Liu"},{"first_name":"Alexandre","last_name":"Alahi","full_name":"Alahi, Alexandre"},{"full_name":"Russell, Chris","first_name":"Chris","last_name":"Russell"},{"full_name":"Horn, Max","last_name":"Horn","first_name":"Max"},{"full_name":"Zietlow, Dominik","first_name":"Dominik","last_name":"Zietlow"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"}],"oa_version":"Preprint","month":"04","type":"conference","abstract":[{"lang":"eng","text":"Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work."}],"date_updated":"2023-09-13T09:23:08Z","day":"12","arxiv":1,"title":"Causal triplet: An open challenge for intervention-centric causal representation learning","date_created":"2023-08-22T14:20:18Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrLo"}],"year":"2023","_id":"14214","publication":"2nd Conference on Causal Learning and Reasoning","article_processing_charge":"No","oa":1,"publication_status":"published","date_published":"2023-04-12T00:00:00Z","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2301.05169"}],"status":"public","external_id":{"arxiv":["2301.05169"]},"conference":{"start_date":"2023-04-11","location":"Tübingen, Germany","name":"CLeaR: Conference on Causal Learning and Reasoning","end_date":"2023-04-14"},"extern":"1","language":[{"iso":"eng"}],"citation":{"ama":"Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","mla":"Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023. Causal triplet: An open challenge for intervention-centric causal representation learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","apa":"Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &#38; Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric causal representation learning. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","ieee":"Y. Liu <i>et al.</i>, “Causal triplet: An open challenge for intervention-centric causal representation learning,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","chicago":"Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","short":"Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023."}}]