Timezone: »
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 Information
Marco Fumero (La Sapienza, University of Rome)
Florian Wenzel (AWS)
Luca Zancato (University of Padova)
Alessandro Achille (California Institute of Technology)
Emanuele Rodola (Sapienza University of Rome)
Stefano Soatto (UCLA)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)
Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.
Francesco Locatello (Amazon Web Services)
More from the Same Authors
-
2021 : On the Fairness of Causal Algorithmic Recourse »
Julius von Kügelgen · Amir-Hossein Karimi · Umang Bhatt · Isabel Valera · Adrian Weller · Bernhard Schölkopf · Amir-Hossein Karimi -
2021 : Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects »
Julius von Kügelgen · Nikita Agarwal · Jakob Zeitler · Afsaneh Mastouri · Bernhard Schölkopf -
2021 : Representation Learning for Out-of-distribution Generalization in Downstream Tasks »
Frederik Träuble · Andrea Dittadi · Manuel Wuthrich · Felix Widmaier · Peter V Gehler · Ole Winther · Francesco Locatello · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer -
2021 : Representation Learning for Out-of-distribution Generalization in Downstream Tasks »
Frederik Träuble · Andrea Dittadi · Manuel Wüthrich · Felix Widmaier · Peter Gehler · Ole Winther · Francesco Locatello · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer -
2021 : Lie interventions in complex systems with cycles »
Michel Besserve · Bernhard Schölkopf -
2022 : Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee »
Yassine Nemmour · Heiner Kremer · Bernhard Schölkopf · Jia-Jie Zhu -
2023 : Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features »
Cian Eastwood · Shashank Singh · Andrei Nicolicioiu · Marin Vlastelica · Julius von Kügelgen · Bernhard Schölkopf -
2023 : Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding »
Alizée Pace · Hugo Yèche · Bernhard Schölkopf · Gunnar Ratsch · Guy Tennenholtz -
2023 : Learning Linear Causal Representations from Interventions under General Nonlinear Mixing »
Simon Buchholz · Goutham Rajendran · Elan Rosenfeld · Bryon Aragam · Bernhard Schölkopf · Pradeep Ravikumar -
2023 : Continuous Vector Quantile Regression »
Sanketh Vedula · Irene Tallini · Aviv A. Rosenberg · Marco Pegoraro · Emanuele Rodola · Yaniv Romano · Alexander Bronstein -
2023 : Vector Quantile Regression on Manifolds »
Marco Pegoraro · Sanketh Vedula · Aviv A. Rosenberg · Irene Tallini · Emanuele Rodola · Alexander Bronstein -
2023 : Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding »
Alizée Pace · Hugo Yèche · Bernhard Schölkopf · Gunnar Ratsch · Guy Tennenholtz -
2023 : Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding »
Alizée Pace · Hugo Yèche · Bernhard Schölkopf · Gunnar Ratsch · Guy Tennenholtz -
2023 : Learning Linear Causal Representations from Interventions under General Nonlinear Mixing »
Simon Buchholz · Goutham Rajendran · Elan Rosenfeld · Bryon Aragam · Bernhard Schölkopf · Pradeep Ravikumar -
2023 : Explanatory Learning: Towards Artificial Scientific Discovery »
Antonio Norelli · Giorgio Mariani · Luca Moschella · Andrea Santilli · Giambattista Parascandolo · Simone Melzi · Emanuele Rodola -
2023 : Flow Matching for Scalable Simulation-Based Inference »
Jonas Wildberger · Maximilian Dax · Simon Buchholz · Stephen R. Green · Jakob Macke · Bernhard Schölkopf -
2023 : Learning Linear Causal Representations from Interventions under General Nonlinear Mixing »
Simon Buchholz · Goutham Rajendran · Elan Rosenfeld · Bryon Aragam · Bernhard Schölkopf · Pradeep Ravikumar -
2023 : Flow Matching for Scalable Simulation-Based Inference »
Jonas Wildberger · Maximilian Dax · Simon Buchholz · Stephen R. Green · Jakob Macke · Bernhard Schölkopf -
2023 : Distribution Shifts in Generalist and Causal Models »
Francesco Locatello -
2023 : Infusing invariances in neural representations »
Irene Cannistraci · Marco Fumero · Luca Moschella · Valentino Maiorca · Emanuele Rodola -
2023 : Desiderata for Representation Learning from Identifiability, Disentanglement, and Group-Structuredness »
Hamza Keurti · Patrik Reizinger · Bernhard Schölkopf · Wieland Brendel -
2023 Poster: Provably Learning Object-Centric Representations »
Jack Brady · Roland S. Zimmermann · Yash Sharma · Bernhard Schölkopf · Julius von Kügelgen · Wieland Brendel -
2023 Poster: Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries »
Charlotte Loh · Seungwook Han · Shivchander Sudalairaj · Rumen Dangovski · Kai Xu · Florian Wenzel · Marin Soljačić · Akash Srivastava -
2023 Poster: On the Identifiability and Estimation of Causal Location-Scale Noise Models »
Alexander Immer · Christoph Schultheiss · Julia Vogt · Bernhard Schölkopf · Peter Bühlmann · Alexander Marx -
2023 Poster: On Data Manifolds Entailed by Structural Causal Models »
Ricardo Dominguez-Olmedo · Amir-Hossein Karimi · Georgios Arvanitidis · Bernhard Schölkopf -
2023 Poster: The Hessian perspective into the Nature of Convolutional Neural Networks »
Sidak Pal Singh · Thomas Hofmann · Bernhard Schölkopf -
2023 Poster: Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels »
Alexander Immer · Tycho van der Ouderaa · Mark van der Wilk · Gunnar Ratsch · Bernhard Schölkopf -
2023 Poster: On the Relationship Between Explanation and Prediction: A Causal View »
Amir-Hossein Karimi · Krikamol Muandet · Simon Kornblith · Bernhard Schölkopf · Been Kim -
2023 Poster: Diffusion Based Representation Learning »
Sarthak Mittal · Korbinian Abstreiter · Stefan Bauer · Bernhard Schölkopf · Arash Mehrjou -
2023 Poster: Discrete Key-Value Bottleneck »
Frederik Träuble · Anirudh Goyal · Nasim Rahaman · Michael Mozer · Kenji Kawaguchi · Yoshua Bengio · Bernhard Schölkopf -
2023 Oral: Provably Learning Object-Centric Representations »
Jack Brady · Roland S. Zimmermann · Yash Sharma · Bernhard Schölkopf · Julius von Kügelgen · Wieland Brendel -
2023 Poster: Estimation Beyond Data Reweighting: Kernel Method of Moments »
Heiner Kremer · Yassine Nemmour · Bernhard Schölkopf · Jia-Jie Zhu -
2023 Poster: Homomorphism AutoEncoder --- Learning Group Structured Representations from Observed Transitions »
Hamza Keurti · Hsiao-Ru Pan · Michel Besserve · Benjamin F. Grewe · Bernhard Schölkopf -
2022 : Deriving modular inductive biases from the principle of independent mechanisms »
Francesco Locatello -
2022 : Invited talks I, Q/A »
Bernhard Schölkopf · David Lopez-Paz -
2022 : Invited Talks 1, Bernhard Schölkopf and David Lopez-Paz »
Bernhard Schölkopf · David Lopez-Paz -
2022 Poster: Action-Sufficient State Representation Learning for Control with Structural Constraints »
Biwei Huang · Chaochao Lu · Liu Leqi · Jose Miguel Hernandez-Lobato · Clark Glymour · Bernhard Schölkopf · Kun Zhang -
2022 Poster: Generalization and Robustness Implications in Object-Centric Learning »
Andrea Dittadi · Samuele Papa · Michele De Vita · Bernhard Schölkopf · Ole Winther · Francesco Locatello -
2022 Spotlight: Action-Sufficient State Representation Learning for Control with Structural Constraints »
Biwei Huang · Chaochao Lu · Liu Leqi · Jose Miguel Hernandez-Lobato · Clark Glymour · Bernhard Schölkopf · Kun Zhang -
2022 Spotlight: Generalization and Robustness Implications in Object-Centric Learning »
Andrea Dittadi · Samuele Papa · Michele De Vita · Bernhard Schölkopf · Ole Winther · Francesco Locatello -
2022 Poster: Causal Inference Through the Structural Causal Marginal Problem »
Luigi Gresele · Julius von Kügelgen · Jonas Kübler · Elke Kirschbaum · Bernhard Schölkopf · Dominik Janzing -
2022 Poster: Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions »
Heiner Kremer · Jia-Jie Zhu · Krikamol Muandet · Bernhard Schölkopf -
2022 Poster: On the Adversarial Robustness of Causal Algorithmic Recourse »
Ricardo Dominguez-Olmedo · Amir-Hossein Karimi · Bernhard Schölkopf -
2022 Spotlight: Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions »
Heiner Kremer · Jia-Jie Zhu · Krikamol Muandet · Bernhard Schölkopf -
2022 Spotlight: Causal Inference Through the Structural Causal Marginal Problem »
Luigi Gresele · Julius von Kügelgen · Jonas Kübler · Elke Kirschbaum · Bernhard Schölkopf · Dominik Janzing -
2022 Spotlight: On the Adversarial Robustness of Causal Algorithmic Recourse »
Ricardo Dominguez-Olmedo · Amir-Hossein Karimi · Bernhard Schölkopf -
2021 Poster: Function Contrastive Learning of Transferable Meta-Representations »
Muhammad Waleed Gondal · Shruti Joshi · Nasim Rahaman · Stefan Bauer · Manuel Wuthrich · Bernhard Schölkopf -
2021 Spotlight: Function Contrastive Learning of Transferable Meta-Representations »
Muhammad Waleed Gondal · Shruti Joshi · Nasim Rahaman · Stefan Bauer · Manuel Wuthrich · Bernhard Schölkopf -
2021 Poster: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Poster: Learning disentangled representations via product manifold projection »
Marco Fumero · Luca Cosmo · Simone Melzi · Emanuele Rodola -
2021 Poster: Bayesian Quadrature on Riemannian Data Manifolds »
Christian Fröhlich · Alexandra Gessner · Philipp Hennig · Bernhard Schölkopf · Georgios Arvanitidis -
2021 Spotlight: Bayesian Quadrature on Riemannian Data Manifolds »
Christian Fröhlich · Alexandra Gessner · Philipp Hennig · Bernhard Schölkopf · Georgios Arvanitidis -
2021 Oral: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Spotlight: Learning disentangled representations via product manifold projection »
Marco Fumero · Luca Cosmo · Simone Melzi · Emanuele Rodola -
2021 Poster: Necessary and sufficient conditions for causal feature selection in time series with latent common causes »
Atalanti Mastakouri · Bernhard Schölkopf · Dominik Janzing -
2021 Poster: Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression »
Junhyung Park · Uri Shalit · Bernhard Schölkopf · Krikamol Muandet -
2021 Spotlight: Necessary and sufficient conditions for causal feature selection in time series with latent common causes »
Atalanti Mastakouri · Bernhard Schölkopf · Dominik Janzing -
2021 Spotlight: Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression »
Junhyung Park · Uri Shalit · Bernhard Schölkopf · Krikamol Muandet -
2021 Poster: Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning »
Sumedh Sontakke · Arash Mehrjou · Laurent Itti · Bernhard Schölkopf -
2021 Spotlight: Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning »
Sumedh Sontakke · Arash Mehrjou · Laurent Itti · Bernhard Schölkopf -
2020 Workshop: Inductive Biases, Invariances and Generalization in Reinforcement Learning »
Anirudh Goyal · Rosemary Nan Ke · Jane Wang · Stefan Bauer · Theophane Weber · Fabio Viola · Bernhard Schölkopf · Stefan Bauer -
2020 Poster: Weakly-Supervised Disentanglement Without Compromises »
Francesco Locatello · Ben Poole · Gunnar Ratsch · Bernhard Schölkopf · Olivier Bachem · Michael Tschannen -
2019 Poster: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness »
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer -
2019 Oral: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness »
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: Kernel Mean Matching for Content Addressability of GANs »
Wittawat Jitkrittum · Wittawat Jitkrittum · Patsorn Sangkloy · Muhammad Waleed Gondal · Amit Raj · James Hays · Bernhard Schölkopf -
2019 Oral: Kernel Mean Matching for Content Addressability of GANs »
Wittawat Jitkrittum · Wittawat Jitkrittum · Patsorn Sangkloy · Patsorn Sangkloy · Muhammad Waleed Gondal · Muhammad Waleed Gondal · Amit Raj · Amit Raj · James Hays · James Hays · Bernhard Schölkopf · Bernhard Schölkopf -
2019 Poster: First-Order Adversarial Vulnerability of Neural Networks and Input Dimension »
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz -
2019 Poster: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2019 Oral: First-Order Adversarial Vulnerability of Neural Networks and Input Dimension »
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz -
2019 Oral: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2018 Poster: Detecting non-causal artifacts in multivariate linear regression models »
Dominik Janzing · Bernhard Schölkopf -
2018 Poster: On Matching Pursuit and Coordinate Descent »
Francesco Locatello · Anant Raj · Sai Praneeth Reddy Karimireddy · Gunnar Ratsch · Bernhard Schölkopf · Sebastian Stich · Martin Jaggi -
2018 Oral: Detecting non-causal artifacts in multivariate linear regression models »
Dominik Janzing · Bernhard Schölkopf -
2018 Oral: On Matching Pursuit and Coordinate Descent »
Francesco Locatello · Anant Raj · Sai Praneeth Reddy Karimireddy · Gunnar Ratsch · Bernhard Schölkopf · Sebastian Stich · Martin Jaggi -
2018 Poster: Tempered Adversarial Networks »
Mehdi S. M. Sajjadi · Giambattista Parascandolo · Arash Mehrjou · Bernhard Schölkopf -
2018 Poster: Differentially Private Database Release via Kernel Mean Embeddings »
Matej Balog · Ilya Tolstikhin · Bernhard Schölkopf -
2018 Oral: Differentially Private Database Release via Kernel Mean Embeddings »
Matej Balog · Ilya Tolstikhin · Bernhard Schölkopf -
2018 Oral: Tempered Adversarial Networks »
Mehdi S. M. Sajjadi · Giambattista Parascandolo · Arash Mehrjou · Bernhard Schölkopf -
2018 Poster: Learning Independent Causal Mechanisms »
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf -
2018 Oral: Learning Independent Causal Mechanisms »
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf -
2017 Invited Talk: Causal Learning »
Bernhard Schölkopf