Cameron Voloshin

Reinforcement Learning · 2019

A researcher in the Science corpus: 2 papers; trusts 69 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 70 citations into it; #682 of 2,242 by trust here, #1,319 by citations
• Artificial Intelligence — 55 citations into it; #7,629 of 35,345 by trust here, #14,718 by citations
• Management Science and Operations Research — 25 citations into it; #2,965 of 11,946 by trust here, #4,430 by citations
• Statistics and Probability — 25 citations into it
• Evaluation & Benchmarks — 11 citations into it; #2,027 of 3,147 by trust here, #2,671 by citations
• Computational Theory and Mathematics — 9 citations into it; #7,642 of 12,182 by trust here, #6,463 by citations
• Control and Systems Engineering — 8 citations into it
• Statistical Learning Theory — 8 citations into it

Whom Cameron Voloshin trusts overall

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

1. R´emi Munos
2. Richard S. Sutton
3. Lihong Li
4. Andrew Barto
5. Nan Jiang
6. Philip S. Thomas
7. Geoffrey E. Hinton
8. Csaba Szepesvari
9. Doina Precup
10. Ofir Nachum

Who trusts Cameron Voloshin overall

1. Nan Jiang
2. T. Paine
3. Ofir Nachum
4. Nathan Kallus
5. Masatoshi Uehara
6. Scott Fujimoto
7. Ilya Kostrikov
8. Michael R. Zhang
9. Rongjun Qin
10. Justin Fu

Papers from Cameron Voloshin's perspective

Ordered by how strongly the researchers Cameron Voloshin 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. Reinforcement Learning: An Introduction
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Batch Policy Learning under Constraints (2019) — 401 citations
• Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning (2019) — 180 citations