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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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Stable Learning via Sparse Variable Independence
The problem of covariate-shift generalization has attracted intensive re...
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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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NICO++: Towards Better Benchmarking for Domain Generalization
Despite the remarkable performance that modern deep neural networks have...
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02/09/2022
Regulatory Instruments for Fair Personalized Pricing
Personalized pricing is a business strategy to charge different prices t...
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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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Domain-Irrelevant Representation Learning for Unsupervised Domain Generalization
Domain generalization (DG) aims to help models trained on a set of sourc...
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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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Heterogeneous Risk Minimization
Machine learning algorithms with empirical risk minimization usually suf...
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04/16/2021
Deep Stable Learning for Out-Of-Distribution Generalization
Approaches based on deep neural networks have achieved striking performa...
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Algorithmic Decision Making with Conditional Fairness
Nowadays fairness issues have raised great concerns in decision-making s...
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Invariant Adversarial Learning for Distributional Robustness
Machine learning algorithms with empirical risk minimization are vulnera...
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06/07/2019
NICO: A Dataset Towards Non-I.I.D. Image Classification
The I.I.D. hypothesis between training data and testing data is the basi...
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