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Causality is a fundamental notion in science and engineering, and one of the fundamental problems in the field is how to find the causal structure or the underlying causal model. For instance, one focus of this workshop is on causal discovery, i.e., how can we discover causal structure over a set of variables from observational data with automated procedures? Another area of interest is how a causal perspective may help understand and solve advanced machine learning problems.
Recent years have seen impressive progress in theoretical and algorithmic developments of causal discovery from various types of data (e.g., from i.i.d. data, under distribution shifts or in nonstationary settings, under latent confounding or selection bias, or with missing data), as well as in practical applications (such as in neuroscience, climate, biology, and epidemiology). However, many practical issues, including confounding, the large scale of the data, the presence of measurement error, and complex causal mechanisms, are still to be properly addressed, to achieve reliable causal discovery in practice.
Moreover, causality-inspired machine learning (in the context of transfer learning, reinforcement learning, deep learning, etc.) leverages ideas from causality to improve generalization, robustness, interpretability, and sample efficiency and is attracting more and more interest in Machine Learning (ML) and Artificial Intelligence. Despite the benefit of the causal view in transfer learning and reinforcement learning, some tasks in ML, such as dealing with adversarial attacks and learning disentangled representations, are closely related to the causal view but are currently underexplored, and cross-disciplinary efforts may facilitate the anticipated progress.
This workshop aims to provide a forum for discussion for researchers and practitioners in machine learning, statistics, healthcare, and other disciplines to share their recent research in causal discovery and to explore the possibility of interdisciplinary collaboration. We also particularly encourage real applications, such as in neuroscience, biology, and climate science, of causal discovery methods.
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After each keynote, there will be 5 minutes for a live Q&A. You may post your questions in Rocket.Chat before or during the keynote time. The poster session and the virtual coffee break will be on Gather.Town. There is no Q&A for orals and spotlight talks, but all papers will attend the poster session and you can interact with authors there. More details will come soon.
Fri 6:50 a.m. - 7:00 a.m.
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Opening Remarks
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Opening remarks
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Fri 7:00 a.m. - 7:30 a.m.
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Keynotes: Aapo Hyvärinen
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Invited talk
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SlidesLive Video » |
Aapo Hyvarinen 🔗 |
Fri 7:30 a.m. - 8:00 a.m.
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Keynotes: Clark Glymour
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Invited talk
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Clark Glymour 🔗 |
Fri 8:00 a.m. - 8:10 a.m.
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Oral: Ashlynn Fuccello
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Oral talk
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SlidesLive Video » |
Panayiotis Benos 🔗 |
Fri 8:10 a.m. - 8:40 a.m.
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Coffee Break & Social on Gather.Town link » | 🔗 |
Fri 8:40 a.m. - 9:10 a.m.
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Keynotes: James Robins
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Invited talk
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james m robins 🔗 |
Fri 9:10 a.m. - 9:20 a.m.
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Oral: Tineke Blom
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Oral talk
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SlidesLive Video » |
Tineke Blom 🔗 |
Fri 9:20 a.m. - 9:30 a.m.
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Oral: Karthikeyan Shanmugam
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Oral talk
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SlidesLive Video » |
Debarun Bhattacharjya 🔗 |
Fri 9:30 a.m. - 10:00 a.m.
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Coffee Break & Social on Gather.Town link » | 🔗 |
Fri 10:00 a.m. - 10:30 a.m.
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Spotlights 1
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Spotlight
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Fri 10:30 a.m. - 11:30 a.m.
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Poster Session 1 (Gather.Town) ( poster session ) link » | 🔗 |
Fri 11:30 a.m. - 12:00 p.m.
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Coffee Break & Social on Gather.Town link » | 🔗 |
Fri 12:00 p.m. - 12:30 p.m.
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Keynotes: Dominik Janzing
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Invited talk
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SlidesLive Video » |
Dominik Janzing 🔗 |
Fri 12:30 p.m. - 1:00 p.m.
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Keynotes: Caroline Uhler
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Invited talk
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Caroline Uhler 🔗 |
Fri 1:00 p.m. - 1:30 p.m.
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Coffee Break & Social on Gather.Town link » | 🔗 |
Fri 1:30 p.m. - 2:00 p.m.
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Keynotes: Karthika Mohan
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Invited talk
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SlidesLive Video » |
Karthika Mohan 🔗 |
Fri 2:00 p.m. - 2:10 p.m.
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Oral: Ignavier Ng
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Oral talk
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SlidesLive Video » |
Ignavier Ng 🔗 |
Fri 2:10 p.m. - 2:40 p.m.
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Coffee Break & Social on Gather.Town link » | 🔗 |
Fri 2:40 p.m. - 3:10 p.m.
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Keynotes: Shohei Shimizu
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Invited talk
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SlidesLive Video » |
Shohei Shimizu 🔗 |
Fri 3:10 p.m. - 3:40 p.m.
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Spotlights 2
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Spotlight
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Fri 3:40 p.m. - 4:40 p.m.
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Poster Session 2 (Gather.Town) ( poster session ) link » | 🔗 |
Fri 4:40 p.m. - 4:50 p.m.
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Closing Remarks
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closing remarks
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Author Information
Biwei Huang (Carnegie Mellon University)
Sara Magliacane (MIT-IBM Watson AI Lab, IBM Research)
Kun Zhang (CMU)
Danielle Belgrave (Microsoft Research)
Elias Bareinboim (Columbia University)
Daniel Malinsky (Johns Hopkins University)
Thomas Richardson (University of Washington)
Christopher Meek (Microsoft Research)
Peter Spirtes (Carnegie Mellon University)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)
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.
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Alexander Neitz · Giambattista Parascandolo · Stefan Bauer · Bernhard Schölkopf -
2017 : Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation »
Alice Oh · Bernhard Schölkopf -
2017 Poster: Avoiding Discrimination through Causal Reasoning »
Niki Kilbertus · Mateo Rojas Carulla · Giambattista Parascandolo · Moritz Hardt · Dominik Janzing · Bernhard Schölkopf -
2017 Poster: Learning Causal Structures Using Regression Invariance »
AmirEmad Ghassami · Saber Salehkaleybar · Negar Kiyavash · Kun Zhang -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2017 Poster: AdaGAN: Boosting Generative Models »
Ilya Tolstikhin · Sylvain Gelly · Olivier Bousquet · Carl-Johann SIMON-GABRIEL · Bernhard Schölkopf -
2016 Poster: Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels »
Ilya Tolstikhin · Bharath Sriperumbudur · Bernhard Schölkopf -
2016 Poster: Consistent Kernel Mean Estimation for Functions of Random Variables »
Carl-Johann Simon-Gabriel · Adam Scibior · Ilya Tolstikhin · Bernhard Schölkopf -
2014 Poster: Recursive Inversion Models for Permutations »
Christopher Meek · Marina Meila -
2014 Poster: Kernel Mean Estimation via Spectral Filtering »
Krikamol Muandet · Bharath Sriperumbudur · Bernhard Schölkopf -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2013 Poster: The Randomized Dependence Coefficient »
David Lopez-Paz · Philipp Hennig · Bernhard Schölkopf -
2013 Poster: Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. »
Michel Besserve · Nikos K Logothetis · Bernhard Schölkopf -
2013 Poster: Causal Inference on Time Series using Restricted Structural Equation Models »
Jonas Peters · Dominik Janzing · Bernhard Schölkopf -
2012 Poster: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Spotlight: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Poster: The representer theorem for Hilbert spaces: a necessary and sufficient condition »
Francesco Dinuzzo · Bernhard Schölkopf -
2011 Workshop: Philosophy and Machine Learning »
Marcello Pelillo · Joachim M Buhmann · Tiberio Caetano · Bernhard Schölkopf · Larry Wasserman -
2011 Workshop: Cosmology meets Machine Learning »
Michael Hirsch · Sarah Bridle · Bernhard Schölkopf · Phil Marshall · Stefan Harmeling · Mark Girolami -
2011 Invited Talk: From kernels to causal inference »
Bernhard Schölkopf -
2011 Poster: Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance »
Peter Gehler · Carsten Rother · Martin Kiefel · Lumin Zhang · Bernhard Schölkopf -
2011 Poster: A Model for Temporal Dependencies in Event Streams »
Asela Gunawardana · Christopher Meek · Puyang Xu -
2011 Poster: Causal Discovery with Cyclic Additive Noise Models »
Joris M Mooij · Dominik Janzing · Tom Heskes · Bernhard Schölkopf -
2010 Spotlight: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2010 Spotlight: Exact inference and learning for cumulative distribution functions on loopy graphs »
Jim C Huang · Nebojsa Jojic · Christopher Meek -
2010 Poster: Exact inference and learning for cumulative distribution functions on loopy graphs »
Jim C Huang · Nebojsa Jojic · Christopher Meek -
2010 Poster: Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake »
Stefan Harmeling · Michael Hirsch · Bernhard Schölkopf -
2010 Poster: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2010 Poster: Probabilistic latent variable models for distinguishing between cause and effect »
Joris M Mooij · Oliver Stegle · Dominik Janzing · Kun Zhang · Bernhard Schölkopf -
2009 Workshop: Connectivity Inference in Neuroimaging »
Karl Friston · Moritz Grosse-Wentrup · Uta Noppeney · Bernhard Schölkopf -
2009 Poster: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Oral: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Poster: Nonlinear directed acyclic structure learning with weakly additive noise models »
Robert E Tillman · Arthur Gretton · Peter Spirtes -
2008 Workshop: Causality: objectives and assessment »
Isabelle Guyon · Dominik Janzing · Bernhard Schölkopf -
2008 Mini Symposium: Computational Photography »
Bill Freeman · Bernhard Schölkopf -
2008 Poster: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Oral: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Poster: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Poster: Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance »
Jeremy Hill · Jason Farquhar · Suzanne Martens · Felix Bießmann · Bernhard Schölkopf -
2008 Poster: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences »
Matthias Seeger · Hannes Nickisch · Rolf Pohmann · Bernhard Schölkopf -
2008 Poster: MAS: a multiplicative approximation scheme for probabilistic inference »
Ydo Wexler · Christopher Meek -
2008 Spotlight: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Oral: MAS: a multiplicative approximation scheme for probabilistic inference »
Ydo Wexler · Christopher Meek -
2008 Spotlight: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences »
Matthias Seeger · Hannes Nickisch · Rolf Pohmann · Bernhard Schölkopf -
2008 Spotlight: Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance »
Jeremy Hill · Jason Farquhar · Suzanne Martens · Felix Bießmann · Bernhard Schölkopf -
2008 Poster: An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis »
Gabriele B Schweikert · Christian Widmer · Bernhard Schölkopf · Gunnar Rätsch -
2008 Poster: Diffeomorphic Dimensionality Reduction »
Christian Walder · Bernhard Schölkopf -
2007 Spotlight: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Poster: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf -
2007 Poster: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Spotlight: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf -
2007 Spotlight: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Poster: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions »
Christian Walder · Bernhard Schölkopf · Olivier Chapelle -
2006 Poster: Learning Dense 3D Correspondence »
Florian Steinke · Bernhard Schölkopf · Volker Blanz -
2006 Poster: A Local Learning Approach for Clustering »
Mingrui Wu · Bernhard Schölkopf -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: A Nonparametric Approach to Bottom-Up Visual Saliency »
Wolf Kienzle · Felix A Wichmann · Bernhard Schölkopf · Matthias Franz -
2006 Poster: Learning with Hypergraphs: Clustering, Classification, and Embedding »
Denny Zhou · Jiayuan Huang · Bernhard Schölkopf