Changjian Shui

On Learning Fairness and Accuracy on Multiple Subgroups

Changjian Shui

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Tal Arbel

Boyu Wang

Christian Gagné

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (voir plus)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)

doi.org

openreview.net

On the benefits of representation regularization in invariance based domain generalization

Changjian Shui

Boyu Wang

Christian Gagné

A crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Dom… (voir plus)ain generalization aims to alleviate such a prediction gap between the observed and unseen environments. Previous approaches commonly incorporated learning the invariant representation for achieving good empirical performance. In this paper, we reveal that merely learning the invariant representation is vulnerable to the related unseen environment. To this end, we derive a novel theoretical analysis to control the unseen test environment error in the representation learning, which highlights the importance of controlling the smoothness of representation. In practice, our analysis further inspires an efficient regularization method to improve the robustness in domain generalization. The proposed regularization is orthogonal to and can be straightforwardly adopted in existing domain generalization algorithms that ensure invariant representation learning. Empirical results show that our algorithm outperforms the base versions in various datasets and invariance criteria.

2021-12-31

Machine Learning (publié)

doi.org

arxiv.org

Deep Active Learning: Unified and Principled Method for Query and Training

Changjian Shui

Fan Zhou

Christian Gagné

Boyu Wang

2020-06-02

Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press