13 citations · 26 across the 9 of their papers we have counts for
9 papers
Provably Efficient Reinforcement Learning via Surprise Bound
Hanlin Zhu, Ruosong Wang, Jason D. Lee
Value function approximation is important in modern reinforcement learning (RL) problems especially when the state space is (infinitely) large. Despite the importance and wide appl…
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
Hanlin Zhu, Amy Zhang
Goal-conditioned reinforcement learning (GCRL) refers to learning general-purpose skills that aim to reach diverse goals. In particular, offline GCRL only requires purely pre-colle…
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
Hanlin Zhu, Paria Rashidinejad, Jiantao Jiao
We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient d…
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Paria Rashidinejad, Hanlin Zhu, Kunhe Yang +2
Offline reinforcement learning (RL), which refers to decision-making from a previously-collected dataset of interactions, has received significant attention over the past years. Mu…
Average-Case Communication Complexity of Statistical Problems
Cyrus Rashtchian, David P. Woodruff, Peng Ye +1
We study statistical problems, such as planted clique, its variants, and sparse principal component analysis in the context of average-case communication complexity. Our motivation…
Voltage-Controlled Reversible Modulation of Colloidal Quantum Dot Thin Film Photoluminescence
Sihan Xie, Han Zhu, Melissa Li +1
Active modulation of quantum dot thin film photoluminescence (PL) has far-reaching potential applications in biomedical and optoelectronic systems, but challenges remain in achievi…
Vector-Matrix-Vector Queries for Solving Linear Algebra, Statistics, and Graph Problems
Cyrus Rashtchian, David P. Woodruff, Hanlin Zhu
We consider the general problem of learning about a matrix through vector-matrix-vector queries. These queries provide the value of $\boldsymbol{u}^{\mathrm{T}}\boldsymbol{M}\bolds…
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking
Hanlin Zhu, Xue Li, Liuyang Sun +5
Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and spe…
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Ryuichi Takanobu, Hanlin Zhu, Minlie Huang
Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learni…