Gaurav Mahajan

Reinforcement Learning · 2019

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 57 citations into it
• Artificial Intelligence — 54 citations into it; #15,451 of 35,345 by trust here, #20,836 by citations
• Computational Theory and Mathematics — 20 citations into it
• Optimization & Training — 19 citations into it
• Management Science and Operations Research — 15 citations into it
• Computational Mechanics — 14 citations into it
• Statistical Learning Theory — 12 citations into it
• Computer Networks and Communications — 10 citations into it

Whom Gaurav Mahajan trusts overall

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

1. Sham M. Kakade
2. R´emi Munos
3. Richard S. Sutton
4. Bruno Scherrer
5. Ronald J. Williams
6. Justin A. Boyan
7. Nicolò Cesa-Bianchi
8. Yurii E. Nesterov
9. Dimitri P. Bertsekas
10. John Schulman

Who trusts Gaurav Mahajan overall

1. Chi Jin
2. Simon Shaolei Du
3. Andrea Zanette
4. Qi Cai
5. Alekh Agarwal
6. Justin Fu
7. Ruosong Wang

Papers from Gaurav Mahajan's perspective

Ordered by how strongly the researchers Gaurav Mahajan 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. Long Short-Term Memory
6. Asynchronous Methods for Deep Reinforcement Learning
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes (2019) — 345 citations
• On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift (2019) — 663 citations