Distributionally Robust Losses for Latent Covariate Mixtures

28 July 2020

Tatsunori Hashimoto

Papers citing "Distributionally Robust Losses for Latent Covariate Mixtures"

48 / 48 papers shown

Title
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization Jiaqi Wang Yuhang Zhou Zhixiong Zhang Qiguang Chen Yongqiang Chen James Cheng OODD 60 1 0 18 Feb 2025
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization Hexuan Deng Wenxiang Jiao Xuebo Liu Min Zhang Zhaopeng Tu VLM 80 0 0 21 Nov 2024
Fairness in Survival Analysis with Distributionally Robust Optimization Shu Hu George H. Chen 42 4 0 31 Aug 2024
On the KL-Divergence-based Robust Satisficing Model Haojie Yan Minglong Zhou Jiayi Guo 31 0 0 17 Aug 2024
Evaluating Model Performance Under Worst-case Subpopulations Mike Li Hongseok Namkoong Shangzhou Xia 48 17 0 01 Jul 2024
Addressing Polarization and Unfairness in Performative Prediction Kun Jin Tian Xie Yang Liu Xueru Zhang 49 2 0 24 Jun 2024
CherryRec: Enhancing News Recommendation Quality via LLM-driven Framework Shaohuang Wang Lun Wang Yunhan Bu Tianwei Huang 38 2 0 18 Jun 2024
ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations Rwiddhi Chakraborty Adrian Sletten Michael C. Kampffmeyer 34 0 0 20 Mar 2024
Federated Fairness without Access to Sensitive Groups Afroditi Papadaki Natalia Martínez Martín Bertrán Guillermo Sapiro Miguel R. D. Rodrigues FedML 40 2 0 22 Feb 2024
It's All in the Mix: Wasserstein Classification and Regression with Mixed Features Mohammad Reza Belbasi Aras Selvi W. Wiesemann 11 2 0 19 Dec 2023
Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference Kaiwen Hou 37 0 0 30 Nov 2023
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications Jiashuo Liu Jiayun Wu Tianyu Wang Hao Zou Bo-wen Li Peng Cui 13 3 0 08 Nov 2023
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage Kishan Panaganti Zaiyan Xu D. Kalathil Mohammad Ghavamzadeh OOD OffRL 34 6 0 27 Oct 2023
Is this model reliable for everyone? Testing for strong calibration Jean Feng Alexej Gossmann Romain Pirracchio N. Petrick Gene Pennello B. Sahiner 22 3 0 28 Jul 2023
Scaff-PD: Communication Efficient Fair and Robust Federated Learning Yaodong Yu Sai Praneeth Karimireddy Yi Ma Michael I. Jordan FedML 29 3 0 25 Jul 2023
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization G. Iyengar H. Lam Tianyu Wang 28 3 0 16 Jun 2023
Robust Data-driven Prescriptiveness Optimization Mehran Poursoltani Erick Delage A. Georghiou OffRL 14 1 0 09 Jun 2023
Predictive Heterogeneity: Measures and Applications Jiashuo Liu Jiayun Wu Yangqiu Song Peng Cui 25 1 0 01 Apr 2023
Diagnosing Model Performance Under Distribution Shift Tiffany Cai Hongseok Namkoong Steve Yadlowsky 37 27 0 03 Mar 2023
Meta-Learning Mini-Batch Risk Functionals Jacob Tyo Zachary Chase Lipton 22 0 0 27 Jan 2023
Minimax Optimal Estimation of Stability Under Distribution Shift Hongseok Namkoong Yuanzhe Ma Peter Glynn 31 6 0 13 Dec 2022
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation Josh Gardner Zoran Popovic Ludwig Schmidt OOD 29 22 0 23 Nov 2022
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics Shu Hu George H. Chen 22 13 0 18 Nov 2022
Learning from a Biased Sample Roshni Sahoo Lihua Lei Stefan Wager 24 17 0 05 Sep 2022
Long Term Fairness for Minority Groups via Performative Distributionally Robust Optimization Liam Peet-Paré N. Hegde Alona Fyshe FaML 14 4 0 12 Jul 2022
Understanding the effect of sparsity on neural networks robustness Lukas Timpl R. Entezari Hanie Sedghi Behnam Neyshabur O. Saukh 35 12 0 22 Jun 2022
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization Yongqiang Chen Kaiwen Zhou Yatao Bian Binghui Xie Bing Wu ... Kaili Ma Han Yang P. Zhao Bo Han James Cheng OOD OODD 11 33 0 15 Jun 2022
How does overparametrization affect performance on minority groups? Subha Maity Saptarshi Roy Songkai Xue Mikhail Yurochkin Yuekai Sun 26 3 0 07 Jun 2022
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets Nikolaj Thams Michael Oberst David Sontag OOD 38 10 0 31 May 2022
NICO++: Towards Better Benchmarking for Domain Generalization Xingxuan Zhang Linjun Zhou Renzhe Xu Haoxin Liu Zheyan Shen Peng Cui OOD 35 75 0 17 Apr 2022
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning Mayee F. Chen Daniel Y. Fu A. Narayan Michael Zhang Zhao-quan Song Kayvon Fatahalian Christopher Ré SSL 32 47 0 15 Apr 2022
Distributionally Robust Models with Parametric Likelihood Ratios Paul Michel Tatsunori Hashimoto Graham Neubig OOD 27 15 0 13 Apr 2022
Simple and near-optimal algorithms for hidden stratification and multi-group learning Abdoreza Asadpour Daniel J. Hsu 105 20 0 22 Dec 2021
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization Renzhe Xu Xingxuan Zhang Zheyan Shen Tong Zhang Peng Cui OOD 26 26 0 03 Nov 2021
Towards Out-Of-Distribution Generalization: A Survey Jiashuo Liu Zheyan Shen Yue He Xingxuan Zhang Renzhe Xu Han Yu Peng Cui CML OOD 55 517 0 31 Aug 2021
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations Stephen R. Pfohl Haoran Zhang Yizhe Xu Agata Foryciarz Marzyeh Ghassemi N. Shah OOD 29 22 0 27 Aug 2021
Unsupervised Learning of Debiased Representations with Pseudo-Attributes Seonguk Seo Joon-Young Lee Bohyung Han FaML 68 48 0 06 Aug 2021
Preventing dataset shift from breaking machine-learning biomarkers Jéroome Dockes Gaël Varoquaux J B Poline OOD 28 64 0 21 Jul 2021
A Topological-Framework to Improve Analysis of Machine Learning Model Performance Henry Kvinge Colby Wight Sarah Akers Scott Howland W. Choi Xiaolong Ma Luke J. Gosink E. Jurrus K. Kappagantula Tegan H. Emerson 39 0 0 09 Jul 2021
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models G. dÉon Jason dÉon J. R. Wright Kevin Leyton-Brown 25 74 0 01 Jul 2021
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation Agnieszka Słowik Léon Bottou FaML 43 19 0 17 Jun 2021
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization Léo Andéol Yusei Kawakami Yuichiro Wada Takafumi Kanamori K. Müller G. Montavon OOD 37 7 0 09 Jun 2021
Regularizing towards Causal Invariance: Linear Models with Proxies Michael Oberst Nikolaj Thams J. Peters David Sontag OOD 30 23 0 03 Mar 2021
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski 26 33 0 25 Feb 2021
WILDS: A Benchmark of in-the-Wild Distribution Shifts Pang Wei Koh Shiori Sagawa Henrik Marklund Sang Michael Xie Marvin Zhang ... A. Kundaje Emma Pierson Sergey Levine Chelsea Finn Percy Liang OOD 47 1,373 0 14 Dec 2020
Coping with Label Shift via Distributionally Robust Optimisation Junzhe Zhang A. Menon Andreas Veit Srinadh Bhojanapalli Sanjiv Kumar S. Sra OOD 24 70 0 23 Oct 2020
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality Rui Gao 21 88 0 09 Sep 2020
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,087 0 24 Oct 2016