Filippos Christianos

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 7 papers; trusts 239 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it; #18,956 of 35,345 by trust here, #17,776 by citations
• Reinforcement Learning — 157 citations into it; #1,454 of 2,242 by trust here, #1,932 by citations
• Management Science and Operations Research — 36 citations into it; #11,726 of 11,946 by trust here, #10,392 by citations
• Computer Vision and Pattern Recognition — 23 citations into it
• LLM Agents & Tool Use — 18 citations into it
• Game & Decision Theory — 17 citations into it
• Electrical and Electronic Engineering — 16 citations into it
• Computational Theory and Mathematics — 15 citations into it; #10,040 of 12,182 by trust here, #5,954 by citations

Whom Filippos Christianos trusts overall

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

1. Ronald J. Williams
2. Richard S. Sutton
3. Stefano V. Albrecht
4. Volodymyr Mnih
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Ming Tan
8. Jakob N. Foerster
9. Yoshua Bengio
10. Pieter Abbeel

Who trusts Filippos Christianos overall

1. Georgios Papoudakis
2. Chao Yu
3. Stefano V. Albrecht
4. Arrasy Rahman
5. Lukas Schäfer
6. Akash Velu
7. Eugene Vinitsky
8. Chenghao Li
9. Justin K. Terry

Papers from Filippos Christianos's perspective

Ordered by how strongly the researchers Filippos Christianos 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• 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