Stefano V. Albrecht
Artificial Intelligence · 2012–2021
A researcher in the Science corpus: 13 papers; trusts 331 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 205 citations into it; #6,281 of 35,345 by trust here, #9,170 by citations
- Reinforcement Learning — 193 citations into it; #1,453 of 2,242 by trust here, #1,932 by citations
- Management Science and Operations Research — 68 citations into it; #4,140 of 11,946 by trust here, #6,024 by citations
- Computer Vision and Pattern Recognition — 29 citations into it
- Game & Decision Theory — 28 citations into it
- Economics and Econometrics — 27 citations into it
- Sociology and Political Science — 22 citations into it
- LLM Agents & Tool Use — 19 citations into it
Whom Stefano V. Albrecht trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter Stone
- Michael H. Bowling
- Richard S. Sutton
- Geoffrey E. Hinton
- Caroline Claus
- Craig Boutilier
- Christopher J. C. H. Watkins
- Daphne Koller
- Yoshua Bengio
- Michael L. Littman
Who trusts Stefano V. Albrecht overall
- Georgios Papoudakis
- Filippos Christianos
- Arrasy Rahman
- Peter Stone
- Niklas Höpner
- Macheng Shen
- Lukas Schäfer
- S. Assad
- Pablo Hernandez-Leal
- Sriram Srinivasan
Papers from Stefano V. Albrecht's perspective
Ordered by how strongly the researchers Stefano V. Albrecht trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Reinforcement Learning: An Introduction
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Autonomous agents modelling other agents: A comprehensive survey and open problems (2017) — 543 citations
- Reasoning about Hypothetical Agent Behaviours and their Parameters (2017) — 76 citations
- Comparative evaluation of MAL algorithms in a diverse set of ad hoc team problems (2012) — 32 citations
- Reports of the 2016 AAAI Workshop Program (2016) — 3 citations
- A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems (2013) — 134 citations
- Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning (2019) — 244 citations
- Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning (2020) — 230 citations
- Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing (2021) — 176 citations
- Local Information Agent Modelling in Partially-Observable Environments (2021)
- Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning (2020) — 79 citations
- Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks (2020) — 367 citations
- Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms (2020) — 33 citations
- Reports from the 2015 AAAI Workshop Program (2015) — 3 citations