Matteo Pirotta

Reinforcement Learning · 2010–2020

A researcher in the Science corpus: 11 papers; trusts 85 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 105 citations into it; #1,349 of 2,242 by trust here, #1,519 by citations
• Artificial Intelligence — 97 citations into it; #23,396 of 35,345 by trust here, #19,439 by citations
• Management Science and Operations Research — 47 citations into it; #10,687 of 11,946 by trust here, #9,931 by citations
• Optimization & Training — 27 citations into it
• Statistics and Probability — 17 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Statistical Learning Theory — 15 citations into it; #934 of 1,038 by trust here, #920 by citations
• Control and Systems Engineering — 13 citations into it; #6,623 of 8,257 by trust here, #6,083 by citations

Whom Matteo Pirotta trusts overall

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

1. Sham M. Kakade
2. Jan Peters
3. Richard S. Sutton
4. Stefan Schaal
5. Jonathan Baxter
6. Ronald J. Williams
7. Martin L. Puterman
8. Peter L. Bartlett
9. Andrew Barto
10. Dimitri P. Bertsekas

Who trusts Matteo Pirotta overall

1. Matteo Papini
2. Kamil Andrzej Ciosek
3. Rodrigo Nogueira
4. Marcello Restelli
5. Shimon Whiteson
6. Erwan Lecarpentier
7. Mohammad Ghavamzadeh
8. Jannis Bulian
9. Nan Jiang
10. Yinlam Chow

Papers from Matteo Pirotta's perspective

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

Their papers

• Policy gradient in Lipschitz Markov Decision Processes (2015) — 101 citations
• Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning (2018) — 129 citations
• A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces (2020) — 45 citations
• Stochastic Variance-Reduced Policy Gradient (2018) — 213 citations
• Regret Bounds for Kernel-Based Reinforcement Learning (2020) — 22 citations
• Adaptive Batch Size for Safe Policy Gradients (2017) — 37 citations
• Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems (2017) — 20 citations
• Boosted Fitted Q-Iteration (2017) — 49 citations
• Adaptive Step-Size for Policy Gradient Methods (2013) — 79 citations
• Safe Policy Iteration (2013) — 112 citations
• Fitted Policy Search: Direct Policy Search using a Batch Reinforcement Learning Approach (2010) — 5 citations