91 citations · 355 across the 18 of their papers we have counts for
9 papers · 1 filter
Scalable Hierarchical Clustering with Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy +2
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. Th…
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…
Sample Complexity of Learning Mixtures of Sparse Linear Regressions
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor +1
In the problem of learning mixtures of linear regressions, the goal is to learn a collection of signal vectors from a sequence of (possibly noisy) linear measurements, where each m…
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy +1
We propose a new framework for designing estimators for off-policy evaluation in contextual bandits. Our approach is based on the asymptotically optimal doubly robust estimator, bu…
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy +2
We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of…