[{"volume":32,"main_file_link":[{"url":"https://arxiv.org/abs/1906.03292","open_access":"1"}],"extern":"1","status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","date_updated":"2023-09-13T09:46:38Z","year":"2019","citation":{"ista":"Gondal MW, Wüthrich M, Miladinović Đ, Locatello F, Breidt M, Volchkov V, Akpo J, Bachem O, Schölkopf B, Bauer S. 2019. On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 32.","short":"M.W. Gondal, M. Wüthrich, Đ. Miladinović, F. Locatello, M. Breidt, V. Volchkov, J. Akpo, O. Bachem, B. Schölkopf, S. Bauer, in:, Advances in Neural Information Processing Systems, 2019.","mla":"Gondal, Muhammad Waleed, et al. “On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset.” <i>Advances in Neural Information Processing Systems</i>, vol. 32, 2019.","chicago":"Gondal, Muhammad Waleed, Manuel Wüthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset.” In <i>Advances in Neural Information Processing Systems</i>, Vol. 32, 2019.","ieee":"M. W. Gondal <i>et al.</i>, “On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2019, vol. 32.","apa":"Gondal, M. W., Wüthrich, M., Miladinović, Đ., Locatello, F., Breidt, M., Volchkov, V., … Bauer, S. (2019). On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset. In <i>Advances in Neural Information Processing Systems</i> (Vol. 32). Vancouver, Canada.","ama":"Gondal MW, Wüthrich M, Miladinović Đ, et al. On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset. In: <i>Advances in Neural Information Processing Systems</i>. Vol 32. ; 2019."},"date_published":"2019-06-07T00:00:00Z","external_id":{"arxiv":["1906.03292"]},"type":"conference","arxiv":1,"publication_identifier":{"isbn":["9781713807933"]},"day":"07","abstract":[{"lang":"eng","text":"Learning meaningful and compact representations with disentangled semantic\r\naspects is considered to be of key importance in representation learning. Since\r\nreal-world data is notoriously costly to collect, many recent state-of-the-art\r\ndisentanglement models have heavily relied on synthetic toy data-sets. In this\r\npaper, we propose a novel data-set which consists of over one million images of\r\nphysical 3D objects with seven factors of variation, such as object color,\r\nshape, size and position. In order to be able to control all the factors of\r\nvariation precisely, we built an experimental platform where the objects are\r\nbeing moved by a robotic arm. In addition, we provide two more datasets which\r\nconsist of simulations of the experimental setup. These datasets provide for\r\nthe first time the possibility to systematically investigate how well different\r\ndisentanglement methods perform on real data in comparison to simulation, and\r\nhow simulated data can be leveraged to build better representations of the real\r\nworld. We provide a first experimental study of these questions and our results\r\nindicate that learned models transfer poorly, but that model and hyperparameter\r\nselection is an effective means of transferring information to the real world."}],"oa":1,"quality_controlled":"1","language":[{"iso":"eng"}],"conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2019-12-08","end_date":"2019-12-14","location":"Vancouver, Canada"},"_id":"14190","publication":"Advances in Neural Information Processing Systems","author":[{"full_name":"Gondal, Muhammad Waleed","last_name":"Gondal","first_name":"Muhammad Waleed"},{"full_name":"Wüthrich, Manuel","last_name":"Wüthrich","first_name":"Manuel"},{"full_name":"Miladinović, Đorđe","last_name":"Miladinović","first_name":"Đorđe"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Breidt, Martin","first_name":"Martin","last_name":"Breidt"},{"full_name":"Volchkov, Valentin","last_name":"Volchkov","first_name":"Valentin"},{"last_name":"Akpo","first_name":"Joel","full_name":"Akpo, Joel"},{"first_name":"Olivier","last_name":"Bachem","full_name":"Bachem, Olivier"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"full_name":"Bauer, Stefan","first_name":"Stefan","last_name":"Bauer"}],"publication_status":"published","oa_version":"Preprint","article_processing_charge":"No","department":[{"_id":"FrLo"}],"date_created":"2023-08-22T14:09:13Z","title":"On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset","month":"06","intvolume":"        32"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1901.10348"}],"date_published":"2019-12-29T00:00:00Z","type":"conference","oa":1,"publication_identifier":{"isbn":["9781713807933"]},"language":[{"iso":"eng"}],"conference":{"end_date":"2019-12-14","location":"Vancouver, Canada","start_date":"2019-12-08","name":"NeurIPS: Neural Information Processing Systems"},"publication":"Advances in Neural Information Processing Systems","month":"12","oa_version":"Preprint","extern":"1","volume":32,"external_id":{"arxiv":["1901.10348"]},"date_updated":"2023-09-12T08:48:45Z","year":"2019","citation":{"short":"F. Locatello, A. Yurtsever, O. Fercoq, V. Cevher, in:, Advances in Neural Information Processing Systems, 2019, pp. 14291–14301.","mla":"Locatello, Francesco, et al. “Stochastic Frank-Wolfe for Composite Convex Minimization.” <i>Advances in Neural Information Processing Systems</i>, vol. 32, 2019, pp. 14291–14301.","ista":"Locatello F, Yurtsever A, Fercoq O, Cevher V. 2019. Stochastic Frank-Wolfe for composite convex minimization. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 32, 14291–14301.","apa":"Locatello, F., Yurtsever, A., Fercoq, O., &#38; Cevher, V. (2019). Stochastic Frank-Wolfe for composite convex minimization. In <i>Advances in Neural Information Processing Systems</i> (Vol. 32, pp. 14291–14301). Vancouver, Canada.","ama":"Locatello F, Yurtsever A, Fercoq O, Cevher V. Stochastic Frank-Wolfe for composite convex minimization. In: <i>Advances in Neural Information Processing Systems</i>. Vol 32. ; 2019:14291–14301.","ieee":"F. Locatello, A. Yurtsever, O. Fercoq, and V. Cevher, “Stochastic Frank-Wolfe for composite convex minimization,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2019, vol. 32, pp. 14291–14301.","chicago":"Locatello, Francesco, Alp Yurtsever, Olivier Fercoq, and Volkan Cevher. “Stochastic Frank-Wolfe for Composite Convex Minimization.” In <i>Advances in Neural Information Processing Systems</i>, 32:14291–14301, 2019."},"abstract":[{"text":"A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed for the deterministic setting where problem data is readily available. In this setting, generalized conditional gradient methods (aka Frank-Wolfe-type methods) provide scalable solutions by leveraging the so-called linear minimization oracle instead of the projection onto the semidefinite cone. Most problems in machine learning and modern engineering applications, however, contain some degree of stochasticity. In this work, we propose the first conditional-gradient-type method for solving stochastic optimization problems under affine constraints. Our method guarantees O(k−1/3) convergence rate in expectation on the objective residual and O(k−5/12) on the feasibility gap.","lang":"eng"}],"arxiv":1,"day":"29","page":"14291–14301","quality_controlled":"1","author":[{"last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"full_name":"Yurtsever, Alp","first_name":"Alp","last_name":"Yurtsever"},{"last_name":"Fercoq","first_name":"Olivier","full_name":"Fercoq, Olivier"},{"full_name":"Cevher, Volkan","first_name":"Volkan","last_name":"Cevher"}],"_id":"14191","scopus_import":"1","title":"Stochastic Frank-Wolfe for composite convex minimization","intvolume":"        32","publication_status":"published","date_created":"2023-08-22T14:09:35Z","article_processing_charge":"No","department":[{"_id":"FrLo"}]},{"conference":{"location":"Vancouver, Canada","end_date":"2019-12-14","start_date":"2019-12-08","name":"NeurIPS: Neural Information Processing Systems"},"quality_controlled":"1","language":[{"iso":"eng"}],"oa_version":"Preprint","publication_status":"published","article_processing_charge":"No","date_created":"2023-08-22T14:09:53Z","department":[{"_id":"FrLo"}],"month":"05","title":"Are disentangled representations helpful for abstract visual reasoning?","intvolume":"        32","publication":"Advances in Neural Information Processing Systems","_id":"14193","author":[{"first_name":"Sjoerd van","last_name":"Steenkiste","full_name":"Steenkiste, Sjoerd van"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Schmidhuber, Jürgen","last_name":"Schmidhuber","first_name":"Jürgen"},{"full_name":"Bachem, Olivier","last_name":"Bachem","first_name":"Olivier"}],"volume":32,"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1905.12506","open_access":"1"}],"extern":"1","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","arxiv":1,"publication_identifier":{"isbn":["9781713807933"]},"day":"29","abstract":[{"text":"A disentangled representation encodes information about the salient factors\r\nof variation in the data independently. Although it is often argued that this\r\nrepresentational format is useful in learning to solve many real-world\r\ndown-stream tasks, there is little empirical evidence that supports this claim.\r\nIn this paper, we conduct a large-scale study that investigates whether\r\ndisentangled representations are more suitable for abstract reasoning tasks.\r\nUsing two new tasks similar to Raven's Progressive Matrices, we evaluate the\r\nusefulness of the representations learned by 360 state-of-the-art unsupervised\r\ndisentanglement models. Based on these representations, we train 3600 abstract\r\nreasoning models and observe that disentangled representations do in fact lead\r\nto better down-stream performance. In particular, they enable quicker learning\r\nusing fewer samples.","lang":"eng"}],"oa":1,"date_updated":"2023-09-12T09:02:43Z","citation":{"ama":"Steenkiste S van, Locatello F, Schmidhuber J, Bachem O. Are disentangled representations helpful for abstract visual reasoning? In: <i>Advances in Neural Information Processing Systems</i>. Vol 32. ; 2019.","apa":"Steenkiste, S. van, Locatello, F., Schmidhuber, J., &#38; Bachem, O. (2019). Are disentangled representations helpful for abstract visual reasoning? In <i>Advances in Neural Information Processing Systems</i> (Vol. 32). Vancouver, Canada.","ieee":"S. van Steenkiste, F. Locatello, J. Schmidhuber, and O. Bachem, “Are disentangled representations helpful for abstract visual reasoning?,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2019, vol. 32.","chicago":"Steenkiste, Sjoerd van, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem. “Are Disentangled Representations Helpful for Abstract Visual Reasoning?” In <i>Advances in Neural Information Processing Systems</i>, Vol. 32, 2019.","mla":"Steenkiste, Sjoerd van, et al. “Are Disentangled Representations Helpful for Abstract Visual Reasoning?” <i>Advances in Neural Information Processing Systems</i>, vol. 32, 2019.","short":"S. van Steenkiste, F. Locatello, J. Schmidhuber, O. Bachem, in:, Advances in Neural Information Processing Systems, 2019.","ista":"Steenkiste S van, Locatello F, Schmidhuber J, Bachem O. 2019. Are disentangled representations helpful for abstract visual reasoning? Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 32."},"year":"2019","date_published":"2019-05-29T00:00:00Z","type":"conference","external_id":{"arxiv":["1905.12506"]}},{"page":"14611–14624","quality_controlled":"1","_id":"14197","scopus_import":"1","author":[{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Abbati, Gabriele","first_name":"Gabriele","last_name":"Abbati"},{"full_name":"Rainforth, Tom","first_name":"Tom","last_name":"Rainforth"},{"last_name":"Bauer","first_name":"Stefan","full_name":"Bauer, Stefan"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"full_name":"Bachem, Olivier","last_name":"Bachem","first_name":"Olivier"}],"publication_status":"published","article_processing_charge":"No","department":[{"_id":"FrLo"}],"date_created":"2023-08-22T14:12:28Z","title":"On the fairness of disentangled representations","intvolume":"        32","volume":32,"extern":"1","date_updated":"2023-09-12T09:37:22Z","year":"2019","citation":{"mla":"Locatello, Francesco, et al. “On the Fairness of Disentangled Representations.” <i>Advances in Neural Information Processing Systems</i>, vol. 32, 2019, pp. 14611–14624.","short":"F. Locatello, G. Abbati, T. Rainforth, S. Bauer, B. Schölkopf, O. Bachem, in:, Advances in Neural Information Processing Systems, 2019, pp. 14611–14624.","ista":"Locatello F, Abbati G, Rainforth T, Bauer S, Schölkopf B, Bachem O. 2019. On the fairness of disentangled representations. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 32, 14611–14624.","ama":"Locatello F, Abbati G, Rainforth T, Bauer S, Schölkopf B, Bachem O. On the fairness of disentangled representations. In: <i>Advances in Neural Information Processing Systems</i>. Vol 32. ; 2019:14611–14624.","apa":"Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., &#38; Bachem, O. (2019). On the fairness of disentangled representations. In <i>Advances in Neural Information Processing Systems</i> (Vol. 32, pp. 14611–14624). Vancouver, Canada.","chicago":"Locatello, Francesco, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem. “On the Fairness of Disentangled Representations.” In <i>Advances in Neural Information Processing Systems</i>, 32:14611–14624, 2019.","ieee":"F. Locatello, G. Abbati, T. Rainforth, S. Bauer, B. Schölkopf, and O. Bachem, “On the fairness of disentangled representations,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2019, vol. 32, pp. 14611–14624."},"external_id":{"arxiv":["1905.13662"]},"arxiv":1,"day":"08","abstract":[{"text":"Recently there has been a significant interest in learning disentangled\r\nrepresentations, as they promise increased interpretability, generalization to\r\nunseen scenarios and faster learning on downstream tasks. In this paper, we\r\ninvestigate the usefulness of different notions of disentanglement for\r\nimproving the fairness of downstream prediction tasks based on representations.\r\nWe consider the setting where the goal is to predict a target variable based on\r\nthe learned representation of high-dimensional observations (such as images)\r\nthat depend on both the target variable and an \\emph{unobserved} sensitive\r\nvariable. We show that in this setting both the optimal and empirical\r\npredictions can be unfair, even if the target variable and the sensitive\r\nvariable are independent. Analyzing the representations of more than\r\n\\num{12600} trained state-of-the-art disentangled models, we observe that\r\nseveral disentanglement scores are consistently correlated with increased\r\nfairness, suggesting that disentanglement may be a useful property to encourage\r\nfairness when sensitive variables are not observed.","lang":"eng"}],"language":[{"iso":"eng"}],"conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2019-12-08","end_date":"2019-12-14","location":"Vancouver, Canada"},"publication":"Advances in Neural Information Processing Systems","oa_version":"Preprint","month":"12","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1905.13662"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2019-12-08T00:00:00Z","type":"conference","publication_identifier":{"isbn":["9781713807933"]},"oa":1}]