9 citations · 17 across the 9 of their papers we have counts for
7 papers
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX
Ali Mortazavi, Junhao Lin, Nishant A. Mehta
In one view of the classical game of prediction with expert advice with binary outcomes, in each round, each expert maintains an adversarially chosen belief and honestly reports th…
An improved regret analysis for UCB-N and TS-N
Nishant A. Mehta
In the setting of stochastic online learning with undirected feedback graphs, Lykouris et al. (2020) previously analyzed the pseudo-regret of the upper confidence bound-based algor…
Adversarial Online Multi-Task Reinforcement Learning
Quan Nguyen, Nishant A. Mehta
We consider the adversarial online multi-task reinforcement learning setting, where in each of episodes the learner is given an unknown task taken from a finite set of unkn…
CompAdaGrad: A Compressed, Complementary, Computationally-Efficient Adaptive Gradient Method
Nishant A. Mehta, Alistair Rendell, Anish Varghese +1
The adaptive gradient online learning method known as AdaGrad has seen widespread use in the machine learning community in stochastic and adversarial online learning problems and m…
Generalized Mixability via Entropic Duality
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson +1
Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice. We show that a key property of mixability gen…
From Stochastic Mixability to Fast Rates
Nishant A. Mehta, Robert C. Williamson
Empirical risk minimization (ERM) is a fundamental learning rule for statistical learning problems where the data is generated according to some unknown distribution a…