82 citations · 194 across the 12 of their papers we have counts for
13 papers
Distributional Adversarial Loss
Saba Ahmadi, Siddharth Bhandari, Avrim Blum +2
We initiate the study of a new notion of adversarial loss which we call distributional adversarial loss. In this notion, we assume for each original example, the allowed adversaria…
Understanding Why Generalized Reweighting Does Not Improve Over ERM
Runtian Zhai, Chen Dan, Zico Kolter +1
Empirical risk minimization (ERM) is known in practice to be non-robust to distributional shift where the training and the test distributions are different. A suite of approaches,…
Boosted CVaR Classification
Runtian Zhai, Chen Dan, Arun Sai Suggala +2
Many modern machine learning tasks require models with high tail performance, i.e. high performance over the worst-off samples in the dataset. This problem has been widely studied…
DORO: Distributional and Outlier Robust Optimization
Runtian Zhai, Chen Dan, J. Zico Kolter +1
Many machine learning tasks involve subpopulation shift where the testing data distribution is a subpopulation of the training distribution. For such settings, a line of recent wor…
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Han Zhao, Chen Dan, Bryon Aragam +3
A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invaria…
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification
Chen Dan, Yuting Wei, Pradeep Ravikumar
Adversarial robustness has become a fundamental requirement in modern machine learning applications. Yet, there has been surprisingly little statistical understanding so far. In th…