151 citations · 300 across the 9 of their papers we have counts for
9 papers
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy +1
Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for ep…
Contrastive estimation reveals topic posterior information to linear models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive learning is an approach to representation learning that utilizes naturally occurring similar and dissimilar pairs of data points to find useful embeddings of data. In t…
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz +1
Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity. To iso…
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor +1
We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixt…
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Yining Wang, Ruosong Wang, Simon S. Du +1
We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity a…
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy +1
We present an algorithm, HOMER, for exploration and reinforcement learning in rich observation environments that are summarizable by an unknown latent state space. The algorithm in…