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06/21/2023
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
In stochastic zeroth-order optimization, a problem of practical relevanc...
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06/08/2023
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning
For a federated learning model to perform well, it is crucial to have a ...
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02/24/2022
A Dynamic Fast Gaussian Transform
The Fast Gaussian Transform (FGT) enables subquadratic-time multiplicati...
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02/09/2022
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Sample-efficiency guarantees for offline reinforcement learning (RL) oft...
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07/30/2021
Towards General Function Approximation in Zero-Sum Markov Games
This paper considers two-player zero-sum finite-horizon Markov games wit...
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07/14/2021
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Deep Reinforcement Learning (RL) powered by neural net approximation of ...
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07/09/2021
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Bandit problems with linear or concave reward have been extensively stud...
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05/24/2021
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Policy optimization, which learns the policy of interest by maximizing t...
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05/11/2021
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
Federated Learning (FL) is an emerging learning scheme that allows diffe...
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01/20/2021
Solving Tall Dense SDPs in the Current Matrix Multiplication Time
This paper introduces a new interior point method algorithm that solves ...
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11/24/2020