13 citations · 25 across the 6 of their papers we have counts for
6 papers · 1 filter
Adaptive Oracle-Efficient Online Learning
Guanghui Wang, Zihao Hu, Vidya Muthukumar +1
The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitati…
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective
Adhyyan Narang, Vidya Muthukumar, Anant Sahai
State-of-the-art deep learning classifiers are heavily overparameterized with respect to the amount of training examples and observed to generalize well on "clean" data, but be hig…
Online Model Selection for Reinforcement Learning with Function Approximation
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar +2
Deep reinforcement learning has achieved impressive successes yet often requires a very large amount of interaction data. This result is perhaps unsurprising, as using complicated…
Harmless interpolation of noisy data in regression
Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian +1
A continuing mystery in understanding the empirical success of deep neural networks is their ability to achieve zero training error and generalize well, even when the training data…
Best of many worlds: Robust model selection for online supervised learning
Vidya Muthukumar, Mitas Ray, Anant Sahai +1
We introduce algorithms for online, full-information prediction that are competitive with contextual tree experts of unknown complexity, in both probabilistic and adversarial setti…
Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons
Ashwin Pananjady, Cheng Mao, Vidya Muthukumar +2
Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items,…