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stat.ML2026
Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets
Hyunjoo Kim, Sicheng Wu, Agastya Venkatraman +2
Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models. One impo…
stat.ML2024
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility
Rajdeep Haldar, Yue Xing, Qifan Song +1
Recent works have shown theoretically and empirically that redundant data dimensions are a source of adversarial vulnerability. However, the inverse doesn't seem to hold in practic…
stat.ML2023
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
Jinwon Sohn, Qifan Song, Guang Lin
As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas. This work proposes fai…