17 citations · 17 across the 6 of their papers we have counts for
4 papers · 1 filter
p-Mean Regret for Stochastic Bandits
Anand Krishna, Philips George John, Adarsh Barik +1
In this work, we extend the concept of the -mean welfare objective from social choice theory (Moulin 2004) to study -mean regret in stochastic multi-armed bandit problems. Th…
Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs
Philips George John, Arnab Bhattacharyya, Silviu Maniu +2
Reinforcement learning algorithms are usually stated without theoretical guarantees regarding their performance. Recently, Jin, Yang, Wang, and Jordan (COLT 2020) showed a polynomi…
Learning multivariate Gaussians with imperfect advice
Arnab Bhattacharyya, Davin Choo, Philips George John +1
We revisit the problem of distribution learning within the framework of learning-augmented algorithms. In this setting, we explore the scenario where a probability distribution is…
Distribution Learning Meets Graph Structure Sampling
Arnab Bhattacharyya, Sutanu Gayen, Philips George John +2
This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, usin…