13 citations · 22 across the 3 of their papers we have counts for
5 papers
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…
Class-Weighted Classification: Trade-offs and Robust Approaches
Ziyu Xu, Chen Dan, Justin Khim +1
We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. F…
Learning Complexity of Simulated Annealing
Avrim Blum, Chen Dan, Saeed Seddighin
Simulated annealing is an effective and general means of optimization. It is in fact inspired by metallurgy, where the temperature of a material determines its behavior in thermody…
Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation
Chen Dan, Hong Wang, Hongyang Zhang +2
We study the low rank approximation problem of any given matrix over and in entry-wise loss, that is, finding a rank-…
Adversarially Robust Generalization Just Requires More Unlabeled Data
Runtian Zhai, Tianle Cai, Di He +4
Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbe…