212 citations · 575 across the 20 of their papers we have counts for
16 papers · 1 filter
Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization
Dongruo Zhou
We study the deterministic first-order oracle complexity of finding \(ε\)-stationary points in smooth nonconvex optimization when the objective satisfies higher-order smoothness as…
On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference
Yue Yu, Qiwei Di, Quanquan Gu +1
Test-time compute (TTC) has become an increasingly prominent paradigm for enhancing large language models (LLMs). Despite the empirical success of methods such as best-of- (BoN)…
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
Ruhan Wang, Yu Yang, Zhishuai Liu +2
We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. O…
Batched Neural Bandits
Quanquan Gu, Amin Karbasi, Khashayar Khosravi +2
In many sequential decision-making problems, the individuals are split into several batches and the decision-maker is only allowed to change her policy at the end of batches. These…
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Yue Wu, Dongruo Zhou, Quanquan Gu
We study reinforcement learning in an infinite-horizon average-reward setting with linear function approximation, where the transition probability function of the underlying Markov…
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Dongruo Zhou, Quanquan Gu, Csaba Szepesvari
We study reinforcement learning (RL) with linear function approximation where the underlying transition probability kernel of the Markov decision process (MDP) is a linear mixture…
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Jiafan He, Dongruo Zhou, Quanquan Gu
Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining -type regret bo…
Provable Multi-Objective Reinforcement Learning with Generative Models
Dongruo Zhou, Jiahao Chen, Quanquan Gu
Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple o…
Neural Thompson Sampling
Weitong Zhang, Dongruo Zhou, Lihong Li +1
Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson…
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
Dongruo Zhou, Jiafan He, Quanquan Gu
Modern tasks in reinforcement learning have large state and action spaces. To deal with them efficiently, one often uses predefined feature mapping to represent states and actions…
Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou, Lihong Li, Quanquan Gu
We study the stochastic contextual bandit problem, where the reward is generated from an unknown function with additive noise. No assumption is made about the reward function other…
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
Difan Zou, Yuan Cao, Dongruo Zhou +1
We study the problem of training deep neural networks with Rectified Linear Unit (ReLU) activation function using gradient descent and stochastic gradient descent. In particular, w…
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Dongruo Zhou, Jinghui Chen, Yuan Cao +2
Adaptive gradient methods are workhorses in deep learning. However, the convergence guarantees of adaptive gradient methods for nonconvex optimization have not been thoroughly stud…
Finding Local Minima via Stochastic Nested Variance Reduction
Dongruo Zhou, Pan Xu, Quanquan Gu
We propose two algorithms that can find local minima faster than the state-of-the-art algorithms in both finite-sum and general stochastic nonconvex optimization. At the core of th…
Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou, Pan Xu, Quanquan Gu
We study finite-sum nonconvex optimization problems, where the objective function is an average of nonconvex functions. We propose a new stochastic gradient descent algorithm b…
Stochastic Variance-Reduced Cubic Regularized Newton Method
Dongruo Zhou, Pan Xu, Quanquan Gu
We propose a stochastic variance-reduced cubic regularized Newton method for non-convex optimization. At the core of our algorithm is a novel semi-stochastic gradient along with a…