Towards Causal Representation Learning
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Yoshua Bengio | Francesco Locatello | Anirudh Goyal | Bernhard Scholkopf | Nal Kalchbrenner | Stefan Bauer | Nan Rosemary Ke | Yoshua Bengio | B. Schölkopf | Anirudh Goyal | Nal Kalchbrenner | Francesco Locatello | B. Scholkopf | Stefan Bauer | B. Scholkopf
[1] P. Pronovost,et al. A targeted real-time early warning score (TREWScore) for septic shock , 2015, Science Translational Medicine.
[2] P. Cheng,et al. Causal Learning , 2012 .
[3] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[4] Bernhard Schölkopf,et al. Detecting non-causal artifacts in multivariate linear regression models , 2018, ICML.
[5] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[6] Abigail J. Stewart,et al. Missed opportunities: Psychological ramifications of counterfactual thought in midlife women , 1995 .
[7] Christina Heinze-Deml,et al. Invariant Causal Prediction for Nonlinear Models , 2017, Journal of Causal Inference.
[8] O. Penrose. The Direction of Time , 1962 .
[9] Jimeng Sun,et al. Causal Regularization , 2019, NeurIPS.
[10] Nils Thürey,et al. Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow , 2018, Comput. Graph. Forum.
[11] Bernhard Schölkopf,et al. Telling cause from effect in deterministic linear dynamical systems , 2015, ICML.
[12] Jonas Peters,et al. Causal inference by using invariant prediction: identification and confidence intervals , 2015, 1501.01332.
[13] Bernhard Schölkopf,et al. Identifiability of Causal Graphs using Functional Models , 2011, UAI.
[14] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[15] Daan Wierstra,et al. Recurrent Environment Simulators , 2017, ICLR.
[16] Christopher Joseph Pal,et al. A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms , 2019, ICLR.
[17] Mark Chen,et al. Generative Pretraining From Pixels , 2020, ICML.
[18] Bernhard Schölkopf,et al. Measuring Statistical Dependence with Hilbert-Schmidt Norms , 2005, ALT.
[19] Michael I. Jordan. Graphical Models , 1998 .
[20] R Devon Hjelm,et al. Learning Representations by Maximizing Mutual Information Across Views , 2019, NeurIPS.
[21] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[22] Elias Bareinboim,et al. Transportability from Multiple Environments with Limited Experiments: Completeness Results , 2014, NIPS.
[23] Stefan Bauer,et al. On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset , 2019, NeurIPS.
[24] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[25] Lucas Beyer,et al. Big Transfer (BiT): General Visual Representation Learning , 2020, ECCV.
[26] Yann LeCun,et al. Tangent Prop - A Formalism for Specifying Selected Invariances in an Adaptive Network , 1991, NIPS.
[27] Doina Precup,et al. Off-Policy Deep Reinforcement Learning without Exploration , 2018, ICML.
[28] Yoshua Bengio,et al. An Analysis of the Adaptation Speed of Causal Models , 2020, AISTATS.
[29] Suchi Saria,et al. Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms , 2018, ArXiv.
[30] Ankush Gupta,et al. Unsupervised Learning of Object Keypoints for Perception and Control , 2019, NeurIPS.
[31] Andre Esteva,et al. A guide to deep learning in healthcare , 2019, Nature Medicine.
[32] Bernhard Schölkopf,et al. Causal Inference Using the Algorithmic Markov Condition , 2008, IEEE Transactions on Information Theory.
[33] Bernhard Schölkopf,et al. Generalization in anti-causal learning , 2018, ArXiv.
[34] Bernhard Schölkopf,et al. Invariant Models for Causal Transfer Learning , 2015, J. Mach. Learn. Res..
[35] Stephen C. Adams,et al. Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication , 2020, ArXiv.
[36] Bernhard Schölkopf,et al. Deconfounding Reinforcement Learning in Observational Settings , 2018, ArXiv.
[37] Honglak Lee,et al. Action-Conditional Video Prediction using Deep Networks in Atari Games , 2015, NIPS.
[38] J. Peters,et al. Invariant Causal Prediction for Sequential Data , 2017, Journal of the American Statistical Association.
[39] Dan Geiger,et al. Logical and algorithmic properties of independence and their application to Bayesian networks , 1990, Annals of Mathematics and Artificial Intelligence.
[40] Stefan Bauer,et al. The Arrow of Time in Multivariate Time Series , 2016, ICML.
[41] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[42] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[43] Yoshua Bengio,et al. Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems , 2020, ArXiv.
[44] Anthony Zee,et al. Die Rückseite des Spiegels , 1990 .
[45] Razvan Pascanu,et al. A simple neural network module for relational reasoning , 2017, NIPS.
[46] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[47] Bernhard Schölkopf,et al. Group invariance principles for causal generative models , 2017, AISTATS.
[48] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[49] S. Levine,et al. Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems , 2020, ArXiv.
[50] Fabio Viola,et al. Causally Correct Partial Models for Reinforcement Learning , 2020, ArXiv.
[51] Bernhard Schölkopf,et al. Causal Markov Condition for Submodular Information Measures , 2010, COLT.
[52] R. Matthews. Storks Deliver Babies (p= 0.008) , 2000 .
[53] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[54] Sebastian Ruder,et al. An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.
[55] Elias Bareinboim,et al. Bandits with Unobserved Confounders: A Causal Approach , 2015, NIPS.
[56] Bernhard Schölkopf,et al. Spatially Structured Recurrent Modules , 2021, ICLR.
[57] Bernhard Schölkopf,et al. From Ordinary Differential Equations to Structural Causal Models: the deterministic case , 2013, UAI.
[58] B. Schölkopf,et al. Kernel‐based tests for joint independence , 2016, 1603.00285.
[59] Adrian Weller,et al. On the Fairness of Causal Algorithmic Recourse , 2020, ArXiv.
[60] Georg Heigold,et al. Object-Centric Learning with Slot Attention , 2020, NeurIPS.
[61] Jonathan Tennyson,et al. Water vapour in the atmosphere of the habitable-zone eight-Earth-mass planet K2-18 b , 2019, Nature Astronomy.
[62] Aapo Hyvärinen,et al. A Linear Non-Gaussian Acyclic Model for Causal Discovery , 2006, J. Mach. Learn. Res..
[63] Geoffrey E. Hinton,et al. NeuroAnimator: fast neural network emulation and control of physics-based models , 1998, SIGGRAPH.
[64] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[65] Jonas Peters,et al. Causal Models for Dynamical Systems , 2020, Probabilistic and Causal Inference.
[66] Bernhard Schölkopf,et al. Regression by dependence minimization and its application to causal inference in additive noise models , 2009, ICML '09.
[67] Bernhard Schölkopf,et al. The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA , 2019, UAI.
[68] Alexander Lerchner,et al. COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration , 2019, ArXiv.
[69] Bernhard Schölkopf,et al. Optimal Decision Making Under Strategic Behavior , 2019, ArXiv.
[70] Nicolas Heess,et al. Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search , 2018, ICLR.
[71] Ciarán M Lee,et al. Improving the accuracy of medical diagnosis with causal machine learning , 2020, Nature Communications.
[72] Dacheng Tao,et al. Domain Generalization via Conditional Invariant Representation , 2018, ArXiv.
[73] Wolfgang Spohn,et al. Grundlagen der Entscheidungstheorie , 1978 .
[74] Sebastian Ruder,et al. Universal Language Model Fine-tuning for Text Classification , 2018, ACL.
[75] Elias Bareinboim,et al. Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes , 2019, NeurIPS.
[76] G. Lugosi,et al. Strong Universal Consistency of Neural Network Classifiers , 1993, Proceedings. IEEE International Symposium on Information Theory.
[77] Bernhard Schölkopf,et al. Identifying confounders using additive noise models , 2009, UAI.
[78] Michael C. Mozer,et al. Learning Deep Disentangled Embeddings with the F-Statistic Loss , 2018, NeurIPS.
[79] Daniel Foreman-Mackey,et al. A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE K2 DATA , 2015, 1502.04715.
[80] 知秀 柴田. 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding , 2020 .
[81] Suchi Saria,et al. Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport , 2018, AISTATS.
[82] Bernhard Scholkopf,et al. Structural Autoencoders Improve Representations for Generation and Transfer , 2020, ArXiv.
[83] Bernt Schiele,et al. Not Using the Car to See the Sidewalk — Quantifying and Controlling the Effects of Context in Classification and Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[84] Ben Poole,et al. Weakly-Supervised Disentanglement Without Compromises , 2020, ICML.
[85] Srivatsan Srinivasan,et al. Evaluating Reinforcement Learning Algorithms in Observational Health Settings , 2018, ArXiv.
[86] Bernhard Schölkopf,et al. Avoiding Discrimination through Causal Reasoning , 2017, NIPS.
[87] Luigi Gresele,et al. Learning explanations that are hard to vary , 2020, ArXiv.
[88] Bernhard Schölkopf,et al. Learning Independent Causal Mechanisms , 2017, ICML.
[89] Jessica B. Hamrick,et al. Structured agents for physical construction , 2019, ICML.
[90] Aaron van den Oord,et al. Shaping Belief States with Generative Environment Models for RL , 2019, NeurIPS.
[91] Hazim Kemal Ekenel,et al. How Image Degradations Affect Deep CNN-Based Face Recognition? , 2016, 2016 International Conference of the Biometrics Special Interest Group (BIOSIG).
[92] Matt J. Kusner,et al. Counterfactual Fairness , 2017, NIPS.
[93] Aapo Hyvärinen,et al. On the Identifiability of the Post-Nonlinear Causal Model , 2009, UAI.
[94] S. Gelly,et al. Self-Supervised Learning of Video-Induced Visual Invariances , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[95] Bernhard Schölkopf,et al. A Permutation-Based Kernel Conditional Independence Test , 2014, UAI.
[96] Jure Leskovec,et al. Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.
[97] Arvid Lundervold,et al. An overview of deep learning in medical imaging focusing on MRI , 2018, Zeitschrift fur medizinische Physik.
[98] Stefan Bauer,et al. Learning stable and predictive structures in kinetic systems , 2018, Proceedings of the National Academy of Sciences.
[99] Eduardo F. Morales,et al. An Introduction to Reinforcement Learning , 2011 .
[100] John Asher Johnson,et al. STELLAR AND PLANETARY PROPERTIES OF K2 CAMPAIGN 1 CANDIDATES AND VALIDATION OF 17 PLANETS, INCLUDING A PLANET RECEIVING EARTH-LIKE INSOLATION , 2015, 1503.07866.
[101] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[102] Wojciech M. Czarnecki,et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning , 2019, Nature.
[103] Elias Bareinboim,et al. External Validity: From Do-Calculus to Transportability Across Populations , 2014, Probabilistic and Causal Inference.
[104] Benjamin Recht,et al. Do Image Classifiers Generalize Across Time , 2019 .
[105] Franz von Kutschera,et al. Causation , 1993, J. Philos. Log..
[106] N. Roese,et al. The Functional Theory of Counterfactual Thinking , 2008, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.
[107] Takashi Washio,et al. Error asymmetry in causal and anticausal regression , 2016, Behaviormetrika.
[108] Sriram Vishwanath,et al. Learning Representations by Maximizing Mutual Information in Variational Autoencoders , 2019, 2020 IEEE International Symposium on Information Theory (ISIT).
[109] Razvan Pascanu,et al. Visual Interaction Networks: Learning a Physics Simulator from Video , 2017, NIPS.
[110] Elias Bareinboim,et al. Fairness in Decision-Making - The Causal Explanation Formula , 2018, AAAI.
[111] D. Tao,et al. Deep Domain Generalization via Conditional Invariant Adversarial Networks , 2018, ECCV.
[112] Bernhard Schölkopf,et al. Semi-supervised learning, causality, and the conditional cluster assumption , 2019, UAI.
[113] Henry S. Baird,et al. Document image defect models , 1995 .
[114] Bernhard Scholkopf. Causality for Machine Learning , 2019 .
[115] Chuang Gan,et al. CLEVRER: CoLlision Events for Video REpresentation and Reasoning , 2020, ICLR.
[116] Quoc V. Le,et al. AutoAugment: Learning Augmentation Strategies From Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[117] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[118] Mihaela van der Schaar,et al. Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks , 2018, AAAI.
[119] Demis Hassabis,et al. Mastering Atari, Go, chess and shogi by planning with a learned model , 2019, Nature.
[120] Elizabeth S. Spelke,et al. Principles of Object Perception , 1990, Cogn. Sci..
[121] M. Rowland. The Secret of Our Success. , 2008 .
[122] Bernhard Schölkopf,et al. Inferring deterministic causal relations , 2010, UAI.
[123] Bernhard Schölkopf,et al. From Deterministic ODEs to Dynamic Structural Causal Models , 2016, UAI.
[124] T. Haavelmo,et al. The probability approach in econometrics , 1944 .
[125] Ragnar Frisch,et al. Autonomy Of Economic Relations , 1948 .
[126] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[127] Bernhard Schölkopf,et al. Causal Discovery from Heterogeneous/Nonstationary Data , 2019, J. Mach. Learn. Res..
[128] Rajen Dinesh Shah,et al. The hardness of conditional independence testing and the generalised covariance measure , 2018, The Annals of Statistics.
[129] Alexander J. Smola,et al. Learning with kernels , 1998 .
[130] Shimon Whiteson,et al. Counterfactual Multi-Agent Policy Gradients , 2017, AAAI.
[131] Christopher K. I. Williams,et al. A Framework for the Quantitative Evaluation of Disentangled Representations , 2018, ICLR.
[132] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[133] N. McGlynn. Thinking fast and slow. , 2014, Australian veterinary journal.
[134] Klaus Greff,et al. Multi-Object Representation Learning with Iterative Variational Inference , 2019, ICML.
[135] Alexander D'Amour,et al. On Robustness and Transferability of Convolutional Neural Networks , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[136] Bernhard Schölkopf,et al. Causal Discovery from Temporally Aggregated Time Series , 2017, UAI.
[137] Marcin Andrychowicz,et al. Solving Rubik's Cube with a Robot Hand , 2019, ArXiv.
[138] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[139] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[140] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[141] Bernhard Schölkopf,et al. Information-geometric approach to inferring causal directions , 2012, Artif. Intell..
[142] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[143] Mihaela van der Schaar,et al. Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders , 2019, ICML.
[144] Kevin P. Murphy,et al. Exact Bayesian structure learning from uncertain interventions , 2007, AISTATS.
[145] Nicolai Meinshausen,et al. CAUSALITY FROM A DISTRIBUTIONAL ROBUSTNESS POINT OF VIEW , 2018, 2018 IEEE Data Science Workshop (DSW).
[146] Aleksander Madry,et al. Exploring the Landscape of Spatial Robustness , 2017, ICML.
[147] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[148] Bernhard Schölkopf,et al. Telling cause from effect based on high-dimensional observations , 2009, ICML.
[149] Edward Vul,et al. Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference , 2011, Science.
[150] Daniel Thalmann,et al. Autonomy , 2005, SIGGRAPH Courses.
[151] Tom Schaul,et al. The Predictron: End-To-End Learning and Planning , 2016, ICML.
[152] Jin Tian,et al. Causal Discovery from Changes , 2001, UAI.
[153] Editors , 2003 .
[154] Yee Whye Teh,et al. Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects , 2018, NeurIPS.
[155] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[156] Stefan Bauer,et al. On the Fairness of Disentangled Representations , 2019, NeurIPS.
[157] Ruocheng Guo,et al. A Survey of Learning Causality with Data , 2018, ACM Comput. Surv..
[158] N. Roese. The Functional Basis of Counterfactual Thinking , 1994 .
[159] Blai Bonet,et al. Learning First-Order Symbolic Representations for Planning from the Structure of the State Space , 2020, ECAI.
[160] Suchi Saria,et al. Reliable Decision Support using Counterfactual Models , 2017, NIPS.
[161] Kailash Budhathoki,et al. Origo: causal inference by compression , 2016, Knowledge and Information Systems.
[162] Daniel L. K. Yamins,et al. Flexible Neural Representation for Physics Prediction , 2018, NeurIPS.
[163] Bernhard Schölkopf,et al. Discovering Causal Signals in Images , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[164] Aaron C. Courville,et al. Systematic Generalization: What Is Required and Can It Be Learned? , 2018, ICLR.
[165] Peter V. Gehler,et al. Towards causal generative scene models via competition of experts , 2020, ArXiv.
[166] Luigi Gresele,et al. Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects , 2020, IEEE Transactions on Artificial Intelligence.
[167] Zeb Kurth-Nelson,et al. Causal Reasoning from Meta-reinforcement Learning , 2019, ArXiv.
[168] Bernhard Schölkopf,et al. Artificial intelligence: Learning to see and act , 2015, Nature.
[169] Klaus Greff,et al. On the Binding Problem in Artificial Neural Networks , 2020, ArXiv.
[170] David M. Sobel,et al. A theory of causal learning in children: causal maps and Bayes nets. , 2004, Psychological review.
[171] Wei Chen,et al. Learning to predict the cosmological structure formation , 2018, Proceedings of the National Academy of Sciences.
[172] Aapo Hyvarinen,et al. Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series , 2020, UAI.
[173] Tyler Lu,et al. Impossibility Theorems for Domain Adaptation , 2010, AISTATS.
[174] Mihaela van der Schaar,et al. GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets , 2018, ICLR.
[175] Andriy Mnih,et al. Disentangling by Factorising , 2018, ICML.
[176] Bernhard Schölkopf,et al. Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows , 2017, 2017 IEEE International Conference on Data Mining (ICDM).
[177] Bernhard Schölkopf,et al. Improving the accuracy and speed of support vector learning machines , 1997, NIPS 1997.
[178] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[179] Ole Winther,et al. On the Transfer of Disentangled Representations in Realistic Settings , 2020, ArXiv.
[180] Richard Zhang,et al. Making Convolutional Networks Shift-Invariant Again , 2019, ICML.
[181] Nan Rosemary Ke,et al. Learning Neural Causal Models from Unknown Interventions , 2019, ArXiv.
[182] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[183] Bernhard Schölkopf,et al. Domain Adaptation under Target and Conditional Shift , 2013, ICML.
[184] Thomas G. Dietterich,et al. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , 2018, ICLR.
[185] Alec Radford,et al. Improving Language Understanding by Generative Pre-Training , 2018 .
[186] Andre Cohen,et al. An object-oriented representation for efficient reinforcement learning , 2008, ICML '08.
[187] Chen Sun,et al. Stochastic Prediction of Multi-Agent Interactions from Partial Observations , 2019, ICLR.
[188] Bernhard Schölkopf,et al. Causal Consistency of Structural Equation Models , 2017, UAI.
[189] John R. Slate,et al. Reflective learning: the use of “if only ...” statements to improve performance , 1999 .
[190] Jürgen Schmidhuber,et al. Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions , 2018, ICLR.
[191] Bernhard Schölkopf,et al. Nonlinear causal discovery with additive noise models , 2008, NIPS.
[192] Alexander J. Smola,et al. Detecting and Correcting for Label Shift with Black Box Predictors , 2018, ICML.
[193] Joelle Pineau,et al. Independently Controllable Features , 2017 .
[194] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[195] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[196] Jürgen Schmidhuber,et al. Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.
[197] Yoshua Bengio,et al. Learning a synaptic learning rule , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[198] Boris Katz,et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models , 2019, NeurIPS.
[199] Razvan Pascanu,et al. Deep reinforcement learning with relational inductive biases , 2018, ICLR.
[200] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[201] David Lopez-Paz,et al. Invariant Risk Minimization , 2019, ArXiv.
[202] Bernhard Schölkopf,et al. On Causal Discovery with Cyclic Additive Noise Models , 2011, NIPS.
[203] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[204] Pietro Perona,et al. Multi-Level Cause-Effect Systems , 2015, AISTATS.
[205] B. Schoelkopf,et al. Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference , 2015, 1512.02057.
[206] Yair Weiss,et al. Why do deep convolutional networks generalize so poorly to small image transformations? , 2018, J. Mach. Learn. Res..
[207] Lauren Wilcox,et al. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy , 2020, CHI.
[208] H. Simon,et al. Causal Ordering and Identifiability , 1977 .
[209] Joaquin Quiñonero Candela,et al. Counterfactual reasoning and learning systems: the example of computational advertising , 2013, J. Mach. Learn. Res..
[210] Bernhard Schölkopf,et al. Domain Adaptation with Conditional Transferable Components , 2016, ICML.
[211] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[212] Bernhard Schölkopf,et al. Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination , 2017, IJCAI.
[213] Bernhard Schölkopf,et al. Kernel Methods for Measuring Independence , 2005, J. Mach. Learn. Res..
[214] Bernhard Schölkopf,et al. Causal discovery with continuous additive noise models , 2013, J. Mach. Learn. Res..
[215] Aapo Hyvärinen,et al. Nonlinear independent component analysis: Existence and uniqueness results , 1999, Neural Networks.
[216] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[217] Bernhard Schölkopf,et al. Causal Inference by Choosing Graphs with Most Plausible Markov Kernels , 2006, AI&M.
[218] Sebastian Weichwald,et al. Pragmatism and Variable Transformations in Causal Modelling , 2019 .
[219] Stefan Bauer,et al. Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness , 2018, ICML.
[220] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[221] Tonio Ball,et al. Causal and anti-causal learning in pattern recognition for neuroimaging , 2015, 2014 International Workshop on Pattern Recognition in Neuroimaging.
[222] Joris M. Mooij,et al. Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions , 2017, NeurIPS.
[223] Matthias Bethge,et al. Towards the first adversarially robust neural network model on MNIST , 2018, ICLR.
[224] Yining Chen,et al. Weakly Supervised Disentanglement with Guarantees , 2020, ICLR.
[225] Saumik Bhattacharya,et al. Effects of Degradations on Deep Neural Network Architectures , 2018, ArXiv.
[226] K. Hoover,et al. Causality in Economics and Econometrics , 2006 .
[227] Bernhard Schölkopf,et al. A theory of independent mechanisms for extrapolation in generative models , 2020, AAAI.
[228] Michal Valko,et al. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning , 2020, NeurIPS.
[229] Christina Heinze-Deml,et al. Conditional variance penalties and domain shift robustness , 2017, Machine Learning.
[230] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[231] Bernhard Schölkopf,et al. Semi-supervised interpolation in an anticausal learning scenario , 2015, J. Mach. Learn. Res..
[232] Quoc V. Le,et al. Using Videos to Evaluate Image Model Robustness , 2019, ArXiv.
[233] Jos'e Miguel Hern'andez-Lobato,et al. Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation , 2020, ArXiv.
[234] A. Dawid. Conditional Independence in Statistical Theory , 1979 .
[235] Aapo Hyvärinen,et al. Nonlinear ICA of Temporally Dependent Stationary Sources , 2017, AISTATS.
[236] Sergey Levine,et al. Model-based reinforcement learning with parametrized physical models and optimism-driven exploration , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[237] Pietro Perona,et al. Fast Conditional Independence Test for Vector Variables with Large Sample Sizes , 2018, ArXiv.
[238] Bernhard Schölkopf,et al. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations , 2018, ICML.
[239] Bernhard Schölkopf,et al. Recurrent Independent Mechanisms , 2021, ICLR.
[240] Sjoerd van Steenkiste,et al. Are Disentangled Representations Helpful for Abstract Visual Reasoning? , 2019, NeurIPS.
[241] Tom Burr,et al. Causation, Prediction, and Search , 2003, Technometrics.
[242] Matthew Botvinick,et al. MONet: Unsupervised Scene Decomposition and Representation , 2019, ArXiv.
[243] Ilya Sutskever,et al. Jukebox: A Generative Model for Music , 2020, ArXiv.
[244] Bernhard Schölkopf,et al. Modeling confounding by half-sibling regression , 2016, Proceedings of the National Academy of Sciences.
[245] Pietro Perona,et al. Recognition in Terra Incognita , 2018, ECCV.
[246] R. Zemel,et al. Neural Relational Inference for Interacting Systems , 2018, ICML.
[247] Bernhard Schölkopf,et al. Kernel-based Conditional Independence Test and Application in Causal Discovery , 2011, UAI.
[248] Isabelle Guyon,et al. Causality : Objectives and Assessment , 2010 .
[249] Georg Martius,et al. Variational Autoencoders Pursue PCA Directions (by Accident) , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[250] John Essington,et al. How we learn: why brains learn better than any machine … for now , 2021, Educational Review.
[251] Taesup Kim,et al. Fast AutoAugment , 2019, NeurIPS.
[252] Eric P. Xing,et al. Learning Robust Representations by Projecting Superficial Statistics Out , 2018, ICLR.
[253] Jürgen Schmidhuber,et al. World Models , 2018, ArXiv.
[254] Francesco Locatello,et al. Is Independence all you need? On the Generalization of Representations Learned from Correlated Data , 2020, ArXiv.
[255] Sungjin Ahn,et al. SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition , 2020, ICLR.
[256] Martin A. Riedmiller,et al. Batch Reinforcement Learning , 2012, Reinforcement Learning.
[257] Bernhard Schölkopf,et al. Kernel Measures of Conditional Dependence , 2007, NIPS.
[258] Bernhard Schölkopf,et al. On causal and anticausal learning , 2012, ICML.
[259] Bernhard Schölkopf,et al. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach , 2020, NeurIPS.
[260] Bernhard Schölkopf,et al. Consistency of Causal Inference under the Additive Noise Model , 2013, ICML.
[261] Yoshua Bengio,et al. CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning , 2020, ICLR.
[262] Mihaela van der Schaar,et al. Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design , 2018, ICML.
[263] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[264] I. Guyon,et al. Causal Generative Neural Networks , 2017, 1711.08936.
[265] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[266] Bernhard Schölkopf,et al. Elements of Causal Inference: Foundations and Learning Algorithms , 2017 .
[267] Stephan Günnemann,et al. Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift , 2018, NeurIPS.
[268] Patrice Y. Simard,et al. Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..
[269] Bernhard Schölkopf,et al. Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks , 2014, J. Mach. Learn. Res..
[270] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[271] Jakub W. Pachocki,et al. Dota 2 with Large Scale Deep Reinforcement Learning , 2019, ArXiv.
[272] Alexander S. Ecker,et al. Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming , 2019, ArXiv.
[273] Barbara Solenthaler,et al. Data-driven fluid simulations using regression forests , 2015, ACM Trans. Graph..
[274] Bernhard Schölkopf,et al. Counterfactuals uncover the modular structure of deep generative models , 2018, ICLR.
[275] Bernhard Schölkopf,et al. Towards a Learning Theory of Causation , 2015, 1502.02398.
[276] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[277] Bernhard Schölkopf,et al. Multi-Source Domain Adaptation: A Causal View , 2015, AAAI.