Ruosong Wang

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 12 papers; trusts 295 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 287 citations into it; #4,133 of 35,345 by trust here, #3,907 by citations
• Reinforcement Learning — 166 citations into it
• Computer Vision and Pattern Recognition — 82 citations into it; #4,444 of 21,575 by trust here, #3,393 by citations
• Statistical Learning Theory — 78 citations into it
• Management Science and Operations Research — 76 citations into it; #3,518 of 11,946 by trust here, #2,667 by citations
• Optimization & Training — 71 citations into it
• Scaling & Learning Theory — 64 citations into it; #82 of 428 by trust here, #84 by citations
• Statistics and Probability — 42 citations into it

Whom Ruosong Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. R´emi Munos
2. Sham M. Kakade
3. Richard S. Sutton
4. Dimitri P. Bertsekas
5. Simon Shaolei Du
6. Nan Jiang
7. Geoffrey E. Hinton
8. Alex Krizhevsky
9. Csaba Szepesvari
10. Geoffrey J. Gordon

Who trusts Ruosong Wang overall

1. David Brandfonbrener
2. Alekh Agarwal
3. Simon Shaolei Du
4. Weitong Zhang
5. Yuanzhi Li
6. Sham M. Kakade
7. Greg Yang
8. Zeyuan Allen-Zhu
9. William F. Whitney
10. Nan Jiang

Papers from Ruosong Wang's perspective

Ordered by how strongly the researchers Ruosong Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks (2019) — 1,081 citations
• On Exact Computation with an Infinitely Wide Neural Net (2019) — 1,059 citations
• Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? (2019) — 215 citations
• What are the Statistical Limits of Offline RL with Linear Function Approximation? (2020) — 175 citations
• Enhanced Convolutional Neural Tangent Kernels (2019) — 138 citations
• Preference-based Reinforcement Learning with Finite-Time Guarantees (2020) — 81 citations
• Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? (2020) — 53 citations
• On Reward-Free Reinforcement Learning with Linear Function Approximation (2020) — 119 citations
• Instabilities of Offline RL with Pre-Trained Neural Representation (2021) — 46 citations
• Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks (2019) — 171 citations
• Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle (2019) — 88 citations
• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (2019) — 311 citations