Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

15 June 2022

Papers citing "Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization"

7 / 7 papers shown

Title
Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments Gaojie Jin Ronghui Mu Xinping Yi Xiaowei Huang Lijun Zhang 67 0 0 01 Jul 2024
Mind the Graph When Balancing Data for Fairness or Robustness Jessica Schrouff Alexis Bellot Amal Rannen-Triki Alan Malek Isabela Albuquerque A. Gretton Alexander DÁmour Silvia Chiappa OOD CML 48 1 0 25 Jun 2024
Emerging Synergies in Causality and Deep Generative Models: A Survey Guanglin Zhou Shaoan Xie Guang-Yuan Hao Shiming Chen Erdun Gao Xiwei Xu Chen Wang Liming Zhu Lina Yao Anton van den Hengel AI4CE 55 11 0 29 Jan 2023
Malign Overfitting: Interpolation Can Provably Preclude Invariance Yoav Wald G. Yona Uri Shalit Y. Carmon 17 5 0 28 Nov 2022
A Fine-Grained Analysis on Distribution Shift Olivia Wiles Sven Gowal Florian Stimberg Sylvestre-Alvise Rebuffi Ira Ktena Krishnamurthy Dvijotham A. Cemgil OOD 225 202 0 21 Oct 2021
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 215 901 0 02 Mar 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 184 313 0 07 Feb 2020