Elliot Creager

Artificial Intelligence · with Zemel · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 104 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #10,946 of 35,345 by trust here, #7,062 by citations
• Safety, Robustness & Interpretability — 64 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Representation & Self-Supervised Learning — 48 citations into it; #1,464 of 3,145 by trust here, #1,090 by citations
• Safety Research — 45 citations into it; #1,297 of 5,829 by trust here, #723 by citations
• Sociology and Political Science — 28 citations into it
• AI Ethics, Fairness & Society — 24 citations into it; #135 of 494 by trust here, #75 by citations
• Statistics and Probability — 21 citations into it; #12,697 of 12,932 by trust here, #6,310 by citations

Whom Elliot Creager trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Michael P. Kim
5. Alex Krizhevsky
6. Yann LeCun
7. Jimmy Ba
8. Max Welling
9. Kaiming He
10. Ilya Sutskever

Who trusts Elliot Creager overall

1. Polina Kirichenko
2. Charan Reddy
3. Enyan Dai
4. Yingmin Wu
5. Donald Martin
6. Francesco Locatello
7. Bernhard Schölkopf
8. Evan Zheran Liu
9. Frederik Trauble
10. Hyojin Bahng

Papers from Elliot Creager's perspective

Ordered by how strongly the researchers Elliot Creager 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning Adversarially Fair and Transferable Representations (2018) — 796 citations
• Flexibly Fair Representation Learning by Disentanglement (2019) — 383 citations
• Environment Inference for Invariant Learning (2020) — 469 citations
• Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data (2018) — 146 citations
• Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020) — 72 citations
• Is Independence all you need? On the Generalization of Representations Learned from Correlated Data (2020) — 19 citations
• Gradient-based Optimization of Neural Network Architecture (2018) — 12 citations