Causality for Machine Learning

24 November 2019

Papers citing "Causality for Machine Learning"

47 / 97 papers shown

Title
Causal Multi-Agent Reinforcement Learning: Review and Open Problems St John Grimbly Jonathan P. Shock Arnu Pretorius 44 17 0 12 Nov 2021
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains Christian Gumbsch Martin Volker Butz Georg Martius AI4CE 26 21 0 29 Oct 2021
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL Sumedh Anand Sontakke Stephen Iota Zizhao Hu Arash Mehrjou Laurent Itti Bernhard Schölkopf OODD 20 2 0 29 Oct 2021
Action-Sufficient State Representation Learning for Control with Structural Constraints Erdun Gao Chaochao Lu Liu Leqi José Miguel Hernández-Lobato Clark Glymour Bernhard Schölkopf Kun Zhang 56 32 0 12 Oct 2021
Optimization-based Causal Estimation from Heterogenous Environments Mingzhang Yin Yixin Wang David M. Blei OOD 88 17 0 24 Sep 2021
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond Amir Feder Katherine A. Keith Emaad A. Manzoor Reid Pryzant Dhanya Sridhar ... Roi Reichart Margaret E. Roberts Brandon M Stewart Victor Veitch Diyi Yang CML 41 235 0 02 Sep 2021
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses Keegan Harris Daniel Ngo Logan Stapleton Hoda Heidari Zhiwei Steven Wu 19 31 0 12 Jul 2021
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning Erdun Gao Fan Feng Chaochao Lu Sara Magliacane Kun Zhang 44 66 0 06 Jul 2021
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge Abhin Shah Karthikeyan Shanmugam Kartik Ahuja CML 36 13 0 22 Jun 2021
Causal Navigation by Continuous-time Neural Networks Charles J. Vorbach Ramin Hasani Alexander Amini Mathias Lechner Daniela Rus 26 47 0 15 Jun 2021
Neural graphical modelling in continuous-time: consistency guarantees and algorithms Alexis Bellot K. Branson M. Schaar CML AI4TS 29 44 0 06 May 2021
Causal Learning for Socially Responsible AI Lu Cheng Ahmadreza Mosallanezhad Paras Sheth Huan Liu 85 13 0 25 Apr 2021
Unsuitability of NOTEARS for Causal Graph Discovery Marcus Kaiser Maksim Sipos CML 37 65 0 12 Apr 2021
Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies Timothy E. Lee Jialiang Zhao A. Sawhney Siddharth Girdhar Oliver Kroemer CML 29 32 0 31 Mar 2021
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation Rongguang Wang Pratik Chaudhari Christos Davatzikos OOD 47 50 0 23 Mar 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 38 34 0 15 Mar 2021
Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua Yangze Zhou Bruno Ribeiro OOD 40 108 0 08 Mar 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery M. Vowels Necati Cihan Camgöz Richard Bowden CML 43 297 0 03 Mar 2021
Counterfactual Zero-Shot and Open-Set Visual Recognition Zhongqi Yue Tan Wang Hanwang Zhang Qianru Sun Xiansheng Hua BDL 166 194 0 01 Mar 2021
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game Alexander G. Reisach C. Seiler S. Weichwald CML 27 137 0 26 Feb 2021
Counterfactual Generative Networks Axel Sauer Andreas Geiger OOD BDL CML 43 124 0 15 Jan 2021
Learning Contextual Causality from Time-consecutive Images Hongming Zhang Yintong Huo Xinran Zhao Yangqiu Song Dan Roth CML 32 6 0 13 Dec 2020
On the Binding Problem in Artificial Neural Networks Klaus Greff Sjoerd van Steenkiste Jürgen Schmidhuber OCL 233 255 0 09 Dec 2020
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems N. Sohoni Jared A. Dunnmon Geoffrey Angus Albert Gu Christopher Ré 30 243 0 25 Nov 2020
Learning causal representations for robust domain adaptation shuai Yang Kui Yu Fuyuan Cao Lin Liu Hongya Wang Jiuyong Li OOD CML TTA 20 44 0 12 Nov 2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 77 671 0 06 Nov 2020
Latent Causal Invariant Model Xinwei Sun Botong Wu Xiangyu Zheng Chang-Shu Liu Wei Chen Tao Qin Tie-Yan Liu OOD CML BDL 31 14 0 04 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang-Shu Liu Xinwei Sun Jindong Wang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 35 104 0 03 Nov 2020
Reinforcement Learning of Causal Variables Using Mediation Analysis Tue Herlau Rasmus Larsen OOD CML 32 8 0 29 Oct 2020
Generative Neurosymbolic Machines Jindong Jiang Sungjin Ahn BDL OCL 225 68 0 23 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 25 396 0 19 Oct 2020
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning Ossama Ahmed Frederik Trauble Anirudh Goyal Alexander Neitz Yoshua Bengio Bernhard Schölkopf M. Wuthrich Stefan Bauer CML 37 120 0 08 Oct 2020
Physical System for Non Time Sequence Data Xiong Chen CML 13 0 0 07 Oct 2020
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning Sumedh Anand Sontakke Arash Mehrjou Laurent Itti Bernhard Schölkopf CML 25 60 0 07 Oct 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 46 62 0 11 Sep 2020
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 27 179 0 01 Sep 2020
A causal view of compositional zero-shot recognition Yuval Atzmon Felix Kreuk Uri Shalit Gal Chechik OCL BDL CML 61 118 0 25 Jun 2020
Learning Invariant Representations for Reinforcement Learning without Reconstruction Amy Zhang R. McAllister Roberto Calandra Y. Gal Sergey Levine OOD SSL 60 464 0 18 Jun 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 29 115 0 14 Jun 2020
Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski Daniel Coelho De Castro Ben Glocker CML MedIm 33 232 0 11 Jun 2020
Urban Anomaly Analytics: Description, Detection, and Prediction Mingyang Zhang Tong Li Yue Yu Yong Li Pan Hui Yu Zheng 33 75 0 25 Apr 2020
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey Konstantinos Benidis Syama Sundar Rangapuram Valentin Flunkert Bernie Wang Danielle C. Maddix ... David Salinas Lorenzo Stella François-Xavier Aubet Laurent Callot Tim Januschowski AI4TS 27 176 0 21 Apr 2020
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder Mengyue Yang Furui Liu Zhitang Chen Xinwei Shen Jianye Hao Jun Wang OOD CoGe CML 41 44 0 18 Apr 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 184 314 0 07 Feb 2020
Improving Generalizability of Fake News Detection Methods using Propensity Score Matching Bo Ni Zhichun Guo Jianing Li Meng Jiang 23 12 0 28 Jan 2020
Causality-based Feature Selection: Methods and Evaluations Kui Yu Xianjie Guo Lin Liu Jiuyong Li Hao Wang Zhaolong Ling Xindong Wu CML 24 92 0 17 Nov 2019
From Ordinary Differential Equations to Structural Causal Models: the deterministic case Joris Mooij Dominik Janzing Bernhard Schölkopf 76 101 0 09 Aug 2014