Zhaoran Wang
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 10 papers; trusts 228 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 225 citations into it; #6,170 of 35,345 by trust here, #9,870 by citations
- Reinforcement Learning — 211 citations into it; #1,384 of 2,242 by trust here, #1,778 by citations
- Management Science and Operations Research — 97 citations into it; #1,344 of 11,946 by trust here, #1,695 by citations
- Statistical Learning Theory — 47 citations into it; #913 of 1,038 by trust here, #836 by citations
- Statistics and Probability — 47 citations into it
- Optimization & Training — 44 citations into it
- Computational Theory and Mathematics — 36 citations into it; #5,277 of 12,182 by trust here, #4,696 by citations
- Computer Networks and Communications — 28 citations into it; #6,145 of 9,886 by trust here, #7,492 by citations
Whom Zhaoran Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Csaba Szepesvari
- R´emi Munos
- Andrew Barto
- Geoffrey E. Hinton
- Sham M. Kakade
- Michael L. Littman
- Dimitri P. Bertsekas
- Yoshua Bengio
- Peter L. Bartlett
Who trusts Zhaoran Wang overall
- Qi Cai
- Alekh Agarwal
- Zhuoran Yang
- Masatoshi Uehara
- Ruosong Wang
- Chi Jin
- Qiaomin Xie
- Katja Auernhammer
- Tengyang Xie
- Ahmed Touati
Papers from Zhaoran Wang's perspective
Ordered by how strongly the researchers Zhaoran Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Reinforcement Learning: An Introduction
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Is Pessimism Provably Efficient for Offline RL? (2020) — 437 citations
- Provably Efficient Exploration in Policy Optimization (2019) — 306 citations
- Provably Efficient Reinforcement Learning with Linear Function Approximation (2019) — 700 citations
- A Theoretical Analysis of Deep Q-Learning (2019) — 772 citations
- Sample-Efficient Reinforcement Learning for POMDPs with Linear Function Approximations (2022) — 9 citations
- Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy (2018) — 21 citations
- Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium (2020) — 144 citations
- A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning (2019) — 72 citations
- NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization (2016) — 45 citations
- Blind Attacks on Machine Learners (2016) — 6 citations