Towards Causal Representation Learning

22 February 2021

Bernhard Schölkopf

Francesco Locatello

Stefan Bauer

Papers citing "Towards Causal Representation Learning"

23 / 73 papers shown

Title
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective Yuejiang Liu Riccardo Cadei Jonas Schweizer Sherwin Bahmani Alexandre Alahi OOD TTA 28 51 0 29 Nov 2021
Towards Principled Disentanglement for Domain Generalization Hanlin Zhang Yi-Fan Zhang Weiyang Liu Adrian Weller Bernhard Schölkopf Eric P. Xing OOD 23 112 0 27 Nov 2021
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology Tian Cai Li Xie Muge Chen Yang Liu Di He Shuo-feng Zhang C. Mura P. Bourne Lei Xie OOD 19 4 0 23 Nov 2021
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains Christian Gumbsch Martin Volker Butz Georg Martius AI4CE 18 21 0 29 Oct 2021
Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning Kartik Ahuja Jason S. Hartford Yoshua Bengio 21 38 0 29 Oct 2021
Dynamic Inference with Neural Interpreters Nasim Rahaman Muhammad Waleed Gondal S. Joshi Peter V. Gehler Yoshua Bengio Francesco Locatello Bernhard Schölkopf 31 31 0 12 Oct 2021
Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning Semir Tatlidil Yanqi Liu Emily Sheetz R. I. Bahar Steven Sloman Brown University 19 0 0 09 Oct 2021
Target Languages (vs. Inductive Biases) for Learning to Act and Plan Hector Geffner 34 6 0 15 Sep 2021
Reimagining an autonomous vehicle Jeffrey Hawke E. Haibo Vijay Badrinarayanan Alex Kendall 24 11 0 12 Aug 2021
Did I do that? Blame as a means to identify controlled effects in reinforcement learning Oriol Corcoll Youssef Mohamed Raul Vicente 16 3 0 01 Jun 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization Damien Teney Ehsan Abbasnejad Simon Lucey A. Hengel 23 86 0 12 May 2021
What can the millions of random treatments in nonexperimental data reveal about causes? Andre F. Ribeiro Frank Neffke Ricardo Hausmann CML 15 1 0 03 May 2021
Domain Generalization: A Survey Kaiyang Zhou Ziwei Liu Yu Qiao Tao Xiang Chen Change Loy OOD AI4CE 48 980 0 03 Mar 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi Alexandros Kalousis DRL AI4CE 22 54 0 25 Feb 2021
On the Binding Problem in Artificial Neural Networks Klaus Greff Sjoerd van Steenkiste Jürgen Schmidhuber OCL 224 254 0 09 Dec 2020
On the Fairness of Causal Algorithmic Recourse Julius von Kügelgen Amir-Hossein Karimi Umang Bhatt Isabel Valera Adrian Weller Bernhard Schölkopf FaML 78 82 0 13 Oct 2020
On Disentangled Representations Learned From Correlated Data Frederik Trauble Elliot Creager Niki Kilbertus Francesco Locatello Andrea Dittadi Anirudh Goyal Bernhard Schölkopf Stefan Bauer OOD CML 23 115 0 14 Jun 2020
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 329 1,951 0 04 May 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 173 313 0 07 Feb 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 281 11,681 0 09 Mar 2017
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 238 438 0 01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 278 1,400 0 01 Dec 2016
From Ordinary Differential Equations to Structural Causal Models: the deterministic case Joris Mooij Dominik Janzing Bernhard Schölkopf 71 101 0 09 Aug 2014