65 citations · 367 across the 30 of their papers we have counts for
4 papers · 1 filter
Rank Aggregation via Heterogeneous Thurstone Preference Models
Tao Jin, Pan Xu, Quanquan Gu +1
We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise dist…
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu, Quanquan Gu
Q-learning with neural network function approximation (neural Q-learning for short) is among the most prevalent deep reinforcement learning algorithms. Despite its empirical succes…
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Pan Xu, Felicia Gao, Quanquan Gu
Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradien…
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
Pan Xu, Felicia Gao, Quanquan Gu
We revisit the stochastic variance-reduced policy gradient (SVRPG) method proposed by Papini et al. (2018) for reinforcement learning. We provide an improved convergence analysis o…