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07/11/2023
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
Different distribution shifts require different algorithmic and operatio...
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05/25/2023
Meta Adaptive Task Sampling for Few-Domain Generalization
To ensure the out-of-distribution (OOD) generalization performance, trad...
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05/24/2023
Rethinking the Evaluation Protocol of Domain Generalization
Domain generalization aims to solve the challenge of Out-of-Distribution...
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05/21/2023
Exploring and Exploiting Data Heterogeneity in Recommendation
Massive amounts of data are the foundation of data-driven recommendation...
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04/01/2023
Predictive Heterogeneity: Measures and Applications
As an intrinsic and fundamental property of big data, data heterogeneity...
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06/07/2022
Distributionally Invariant Learning: Rationalization and Practical Algorithms
The invariance property across environments is at the heart of invariant...
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03/27/2022
Towards Domain Generalization in Object Detection
Despite the striking performance achieved by modern detectors when train...
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10/24/2021
Kernelized Heterogeneous Risk Minimization
The ability to generalize under distributional shifts is essential to re...
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08/31/2021
Towards Out-Of-Distribution Generalization: A Survey
Classic machine learning methods are built on the i.i.d. assumption that...
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06/30/2021
Distributionally Robust Learning with Stable Adversarial Training
Machine learning algorithms with empirical risk minimization are vulnera...
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05/09/2021
Heterogeneous Risk Minimization
Machine learning algorithms with empirical risk minimization usually suf...
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06/08/2020
Invariant Adversarial Learning for Distributional Robustness
Machine learning algorithms with empirical risk minimization are vulnera...
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12/20/2019
