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We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g.
On the uniform convergence of relative frequencies of events to their probabilities
V. N. Vapnik and A. Y. Chervonenkis · 1971
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A general lower bound on the number of examples needed for learning
Andrzej Ehrenfeucht, David Haussler, Michael Kearns, and Leslie Valiant · 1989
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Using causal information and local measures to learn bayesian networks
Wai Lam and Fahiem Bacchus · 1993
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Mixture density networks
Christopher M Bishop · 1994
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Learning bayesian networks: The combination of knowledge and statistical data
David Heckerman, Dan Geiger, and David M Chickering · 1995
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Causal diagrams for empirical research
Judea Pearl · 1995
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Learning bayesian networks with local structure
Nir Friedman and Moises Goldszmidt · 1998
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Independent Component Analysis
Aapo Hyvärinen, Juha Karhunen, and Erkki Oja · 2001
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Learning graphical model structure using l1-regularization paths
Mark W. Schmidt, Alexandru Niculescu-Mizil, and Kevin P. Murphy · 2007
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Probabilistic Graphical Models: Principles and Techniques
Daphne Koller and Nir Friedman · 2009
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Causality
Judea Pearl · 2009
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Understanding Machine Learning - from Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Causal inference and the data-fusion problem
Elias Bareinboim and Judea Pearl · 2016
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Deep Learning
Ian J. Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Learning representations for counterfactual inference
Fredrik Johansson, Uri Shalit, and David Sontag · 2016
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Ferran Alet, Tomás Lozano-Pérez, and Leslie P Kaelbling · 2018
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Nonlinear ica using auxiliary variables and generalized contrastive learning
Aapo Hyvärinen, Hiroaki Sasaki, and Richard E. Turner · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2018
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Yoshua Bengio · 2017
Cited alongside, same era.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Learning independent causal mechanisms
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, and Bernhard Schölkopf · 2017
Cited alongside, same era.
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Domain adaptation by using causal inference to predict invariant conditional distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij · 2018
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On First-Order Meta-Learning Algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
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Causal Reasoning from Meta-reinforcement Learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, and Zeb Kurth-Nelson · 2019
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