885 citations · 3.8k across the 104 of their papers we have counts for
6 papers · 2 filters
Greedy Frank-Wolfe Algorithm for Exemplar Selection
Gary Cheng, Armin Askari, Kannan Ramchandran +1
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks. We identify a subset of a data set …
Rademacher Complexity for Adversarially Robust Generalization
Dong Yin, Kannan Ramchandran, Peter Bartlett
Many machine learning models are vulnerable to adversarial attacks; for example, adding adversarial perturbations that are imperceptible to humans can often make machine learning m…
Online Scoring with Delayed Information: A Convex Optimization Viewpoint
Avishek Ghosh, Kannan Ramchandran
We consider a system where agents enter in an online fashion and are evaluated based on their attributes or context vectors. There can be practical situations where this context is…
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Dong Yin, Yudong Chen, Kannan Ramchandran +1
We study robust distributed learning that involves minimizing a non-convex loss function with saddle points. We consider the Byzantine setting where some worker machines have abnor…
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Dong Yin, Yudong Chen, Kannan Ramchandran +1
In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or…
Approximate Ranking from Pairwise Comparisons
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran +1
A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes accor…