Cosmin Paduraru

Reinforcement Learning · 2012–2025

A researcher in the Science corpus: 11 papers; trusts 170 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 345 citations into it; #1,637 of 2,242 by trust here, #965 by citations
• Artificial Intelligence — 344 citations into it; #25,813 of 35,345 by trust here, #8,077 by citations
• Management Science and Operations Research — 101 citations into it; #11,622 of 11,946 by trust here, #3,247 by citations
• Computational Theory and Mathematics — 73 citations into it
• Control and Systems Engineering — 67 citations into it
• Computer Vision and Pattern Recognition — 53 citations into it
• Evaluation & Benchmarks — 42 citations into it
• Electrical and Electronic Engineering — 40 citations into it

Whom Cosmin Paduraru trusts overall

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

1. Richard S. Sutton
2. Sergey Levine
3. Andrew Barto
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Martin A. Riedmiller
7. Yoshua Bengio
8. Doina Precup
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Cosmin Paduraru overall

1. Rafael Figueiredo Prudencio
2. Peter F. Schulam
3. Ilya Kostrikov
4. Ofir Nachum
5. Bing Liu
6. Gabriel Dulac-Arnold
7. Arthur Argenson

Papers from Cosmin Paduraru's perspective

Ordered by how strongly the researchers Cosmin Paduraru 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
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

• RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) — 74 citations
• Hyperparameter Selection for Offline Reinforcement Learning (2020) — 163 citations
• Benchmarks for Deep Off-Policy Evaluation (2021) — 117 citations
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization (2021) — 53 citations
• Safe Exploration in Continuous Action Spaces (2018) — 522 citations
• Challenges of real-world reinforcement learning: definitions, benchmarks and analysis (2021) — 780 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Training Language Models to Self-Correct via Reinforcement Learning (2024) — 429 citations
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020)
• An empirical investigation of the challenges of real-world reinforcement learning (2020) — 140 citations
• An Empirical Analysis of Off-policy Learning in Discrete MDPs (2012) — 8 citations