Changjian Shui

Brennan Nichyporuk

Tal Arbel

2022-08-01

ArXiv (preprint)

Fair Representation Learning through Implicit Path Alignment

Qi CHEN

Jiaqi Li

Boyu Wang

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (published)

openreview.net

Evolving Domain Generalization

Wei Wang

Gezheng Xu

Ruizhi Pu

Jiaqi Li

Fan Zhou

Charles Ling

Boyu Wang

2022-05-31

ArXiv (preprint)

On Learning Fairness and Accuracy on Multiple Subgroups

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Tal Arbel

Boyu Wang

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (see more)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.

openreview.net

On the benefits of representation regularization in invariance based domain generalization

Boyu Wang

2022-01-01

Machine Learning (published)

On the benefits of representation regularization in invariance based domain generalization

Boyu Wang

2021-05-30

ArXiv (preprint)

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

Fan Zhou

Boyu Wang

2020-06-03

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

proceedings.mlr.press

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

Fan Zhou

Boyu Wang

In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning. … (see more)We are providing theoretical insights from the intuition of modeling the interactive procedure in active learning as distribution matching, by adopting the Wasserstein distance. As a consequence, we derived a new training loss from the theoretical analysis, which is decomposed into optimizing deep neural network parameters and batch query selection through alternative optimization. In addition, the loss for training a deep neural network is naturally formulated as a min-max optimization problem through leveraging the unlabeled data information. Moreover, the proposed principles also indicate an explicit uncertainty-diversity trade-off in the query batch selection. Finally, we evaluate our proposed method on different benchmarks, consistently showing better empirical performances and a better time-efficient query strategy compared to the baselines.

2019-11-20

ArXiv (preprint)