26 citations · 38 across the 11 of their papers we have counts for
11 papers
NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereo
Zongrui Li, Qian Zheng, Feishi Wang +3
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. Existing solutions alleviate the ambiguity by either explicitly associa…
Thompson Sampling for Unimodal Bandits
Long Yang, Zhao Li, Zehong Hu +4
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal…
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
Yuxuan Chen, Li Zhang, Shijian Li +1
Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e.g. StarCraft and poker. Neural Fictitious Self…
On Convergence of Gradient Expected Sarsa()
Long Yang, Gang Zheng, Yu Zhang +3
We study the convergence of with linear function approximation. We show that applying the off-line estimate (multi-step bootstrapping) to $\mathtt{Expe…
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points
Long Yang, Qian Zheng, Gang Pan
The goal of policy-based reinforcement learning (RL) is to search the maximal point of its objective. However, due to the inherent non-concavity of its objective, convergence to a…
Gradient Q: A Unified Algorithm with Function Approximation for Reinforcement Learning
Long Yang, Yu Zhang, Qian Zheng +2
Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sarsa) algorithms are efficient and frequently used techniques in reinforcement learning. Q is the firs…
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control
Longxiang Shi, Shijian Li, Longbing Cao +3
In recent years significant progress has been made in dealing with challenging problems using reinforcement learning.Despite its great success, reinforcement learning still faces c…
TBQ(): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
Longxiang Shi, Shijian Li, Longbing Cao +2
Off-policy reinforcement learning with eligibility traces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the…
Field-aware Neural Factorization Machine for Click-Through Rate Prediction
Li Zhang, Weichen Shen, Shijian Li +1
Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction i…
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network
Wenjia Meng, Qian Zheng, Long Yang +2
The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems,…
A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
Long Yang, Minhao Shi, Qian Zheng +2
Recently, a new multi-step temporal learning algorithm, called , unifies -step Tree-Backup (when ) and -step Sarsa (when ) by introducing a sampling parameter…