David Madras

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 7 papers; trusts 67 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #12,456 of 35,345 by trust here, #7,531 by citations
• Safety, Robustness & Interpretability — 58 citations into it
• Safety Research — 41 citations into it; #988 of 5,829 by trust here, #622 by citations
• Sociology and Political Science — 29 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it; #1,463 of 3,145 by trust here, #1,138 by citations
• AI Ethics, Fairness & Society — 18 citations into it; #133 of 494 by trust here, #75 by citations
• Statistics and Probability — 18 citations into it; #12,682 of 12,932 by trust here, #6,310 by citations

Whom David Madras trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Moritz Hardt
5. Jimmy Ba
6. Yahav Bechavod
7. Donald B. Rubin
8. Alex Krizhevsky
9. Michael I. Jordan
10. Yann LeCun

Who trusts David Madras overall

1. Luca Oneto
2. Dana Pessach
3. Donald Martin
4. Enyan Dai
5. Hussein Mozannar
6. Silvia Chiappa
7. Simon Caton
8. Congzheng Song
9. Avishek Joey Bose
10. Guy W. Cole

Papers from David Madras's perspective

Ordered by how strongly the researchers David Madras trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning Adversarially Fair and Transferable Representations (2018) — 796 citations
• Flexibly Fair Representation Learning by Disentanglement (2019) — 383 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data (2018) — 146 citations
• Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer (2017) — 324 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Predict Responsibly: Increasing Fairness by Learning To Defer (2017) — 14 citations