Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

11 June 2021

Kartik Ahuja

Ethan Caballero

Dinghuai Zhang

Jean-Christophe Gagnon-Audet

Ioannis Mitliagkas

Papers citing "Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization"

50 / 56 papers shown

Title
Robust Invariant Representation Learning by Distribution Extrapolation Kotaro Yoshida Konstantinos Slavakis OOD 26 0 0 22 May 2025
Mitigating Spurious Correlations with Causal Logit Perturbation Xiaoling Zhou Wei Ye Rui Xie Shikun Zhang CML 57 0 0 21 May 2025
Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Han Zhang Yan Wang Guanfeng Liu Pengfei Ding Huaxiong Wang Kwok-Yan Lam 45 0 0 20 May 2025
Unsupervised Invariant Risk Minimization Yotam Norman Ron Meir OOD 37 0 0 18 May 2025
Fine-Grained Bias Exploration and Mitigation for Group-Robust Classification Miaoyun Zhao Qiang Zhang C. Li 50 0 0 11 May 2025
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization Jiaqi Wang Yuhang Zhou Zhixiong Zhang Qiguang Chen Yongqiang Chen James Cheng OODD 104 1 0 18 Feb 2025
Achievable distributional robustness when the robust risk is only partially identified Julia Kostin Nicola Gnecco Fanny Yang 106 3 0 04 Feb 2025
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization Wenyu Mao Jiancan Wu Haoyang Liu Yongduo Sui Xiang Wang OOD 67 2 0 03 Aug 2024
Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments Gaojie Jin Ronghui Mu Xinping Yi Xiaowei Huang Lijun Zhang 88 0 0 01 Jul 2024
Feature contamination: Neural networks learn uncorrelated features and fail to generalize Tianren Zhang Chujie Zhao Guanyu Chen Yizhou Jiang Feng Chen OOD MLT OODD 137 4 0 05 Jun 2024
Learning Invariant Causal Mechanism from Vision-Language Models Changwen Zheng Siyu Zhao Xingyu Zhang Jiangmeng Li Changwen Zheng Jingyao Wang CML BDL VLM 56 0 0 24 May 2024
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration Hiroki Naganuma Ryuichiro Hataya Kotaro Yoshida Ioannis Mitliagkas OODD 135 3 0 17 Jul 2023
Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation Kilian Fatras Hiroki Naganuma Ioannis Mitliagkas OOD 32 6 0 22 Jun 2022
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? Dinghuai Zhang Kartik Ahuja Yilun Xu Yisen Wang Aaron Courville OOD 46 96 0 05 Jun 2021
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski 64 35 0 25 Feb 2021
Nonlinear Invariant Risk Minimization: A Causal Approach Chaochao Lu Yuhuai Wu Jośe Miguel Hernández-Lobato Bernhard Schölkopf CML OOD 58 50 0 24 Feb 2021
Model-Based Domain Generalization Alexander Robey George J. Pappas Hamed Hassani OOD 64 130 0 23 Feb 2021
Linear unit-tests for invariance discovery Benjamin Aubin A. Slowik Martín Arjovsky Léon Bottou David Lopez-Paz OOD 30 31 0 22 Feb 2021
Does Invariant Risk Minimization Capture Invariance? Pritish Kamath Akilesh Tangella Danica J. Sutherland Nathan Srebro OOD 244 127 0 04 Jan 2021
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness Sang Michael Xie Ananya Kumar Robbie Jones Fereshte Khani Tengyu Ma Percy Liang OOD 185 62 0 08 Dec 2020
Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki Sekouba Kaba Yoshua Bengio Aaron Courville Doina Precup Guillaume Lajoie MLT 94 261 0 18 Nov 2020
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective Kartik Ahuja Jun Wang Amit Dhurandhar Karthikeyan Shanmugam Kush R. Varshney OOD 59 79 0 30 Oct 2020
Understanding the Failure Modes of Out-of-Distribution Generalization Vaishnavh Nagarajan Anders Andreassen Behnam Neyshabur OOD OODD 33 177 0 29 Oct 2020
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions Kartik Ahuja Karthikeyan Shanmugam Amit Dhurandhar 21 9 0 28 Oct 2020
The Risks of Invariant Risk Minimization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski OOD 48 307 0 12 Oct 2020
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 42 180 0 01 Sep 2020
In Search of Lost Domain Generalization Ishaan Gulrajani David Lopez-Paz OOD 54 1,129 0 02 Jul 2020
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations Zhun Deng Frances Ding Cynthia Dwork Rachel Hong Giovanni Parmigiani Prasad Patil Pragya Sur OOD FaML 32 29 0 20 Jun 2020
Domain Generalization using Causal Matching Divyat Mahajan Shruti Tople Amit Sharma OOD 69 328 0 12 Jun 2020
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 153 2,023 0 16 Apr 2020
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition Vihari Piratla Praneeth Netrapalli Sunita Sarawagi OOD 32 217 0 28 Mar 2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning Andreas Kirsch Clare Lyle Y. Gal 57 16 0 27 Mar 2020
Invariant Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 224 204 0 22 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 282 921 0 02 Mar 2020
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization Vikas Garg Adam Kalai Katrina Ligett Zhiwei Steven Wu OOD 26 22 0 13 Feb 2020
Invariant Risk Minimization Games Kartik Ahuja Karthikeyan Shanmugam Kush R. Varshney Amit Dhurandhar OOD 48 245 0 11 Feb 2020
Domain Generalization Using a Mixture of Multiple Latent Domains Toshihiko Matsuura Tatsuya Harada OOD 53 324 0 18 Nov 2019
Robust Learning with the Hilbert-Schmidt Independence Criterion D. Greenfeld Uri Shalit OOD 30 55 0 01 Oct 2019
Variational Autoencoders and Nonlinear ICA: A Unifying Framework Ilyes Khemakhem Diederik P. Kingma Ricardo Pio Monti Aapo Hyvarinen OOD 48 578 0 10 Jul 2019
Invariant Risk Minimization Martín Arjovsky Léon Bottou Ishaan Gulrajani David Lopez-Paz OOD 144 2,190 0 05 Jul 2019
On Learning Invariant Representation for Domain Adaptation Haiying Zhao Rémi Tachet des Combes Kun Zhang Geoffrey J. Gordon OOD 49 157 0 27 Jan 2019
PAC Learning Guarantees Under Covariate Shift Artidoro Pagnoni Stefan Gramatovici Samuel Liu 25 4 0 16 Dec 2018
Recognition in Terra Incognita Sara Beery Grant Van Horn Pietro Perona 52 835 0 13 Jul 2018
Negative Momentum for Improved Game Dynamics Gauthier Gidel Reyhane Askari Hemmat Mohammad Pezeshki Rémi Le Priol Gabriel Huang Simon Lacoste-Julien Ioannis Mitliagkas AI4CE 38 180 0 12 Jul 2018
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 57 908 0 27 Oct 2017
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data Thorsten Kurth Jian Zhang N. Satish Ioannis Mitliagkas Evan Racah ... J. Deslippe Mikhail Shiryaev Srinivas Sridharan P. Prabhat Pradeep Dubey 19 83 0 17 Aug 2017
Improving Gibbs Sampler Scan Quality with DoGS Ioannis Mitliagkas Lester W. Mackey 35 7 0 18 Jul 2017
Accelerated Stochastic Power Iteration Christopher De Sa Bryan D. He Ioannis Mitliagkas Christopher Ré Peng Xu 62 90 0 10 Jul 2017
YellowFin and the Art of Momentum Tuning Jian Zhang Ioannis Mitliagkas ODL 37 108 0 12 Jun 2017
Deep Variational Information Bottleneck Alexander A. Alemi Ian S. Fischer Joshua V. Dillon Kevin Patrick Murphy 71 1,697 0 01 Dec 2016