Joshua R. Loftus

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 5 papers; trusts 45 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it
• Safety, Robustness & Interpretability — 47 citations into it; #598 of 2,251 by trust here, #378 by citations
• Statistics and Probability — 33 citations into it
• Sociology and Political Science — 32 citations into it
• Safety Research — 30 citations into it
• AI Ethics, Fairness & Society — 25 citations into it; #256 of 494 by trust here, #187 by citations
• Causal Inference & Discovery — 25 citations into it; #223 of 364 by trust here, #138 by citations
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Joshua R. Loftus trusts overall

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

1. Judea Pearl
2. Ilya Shpitser
3. Hirotugu Akaike
4. James M. Robins
5. Tom L. Burr
6. Robert Tibshirani
7. J. Gerring
8. Silvia Chiappa
9. Yoav Benjamini
10. Ming Yuan

Who trusts Joshua R. Loftus overall

1. Robert Tibshirani
2. Silvia Chiappa
3. William Fithian
4. Dana Pessach
5. Jonathan E. Taylor
6. Ninareh Mehrabi
7. M. G'Sell
8. Lucas Rosenblatt
9. Erez Shmueli
10. Niels Bantilan

Papers from Joshua R. Loftus's perspective

Ordered by how strongly the researchers Joshua R. Loftus 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. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. GENERATIVE ADVERSARIAL NETS
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

• Making Decisions that Reduce Discriminatory Impacts (2019) — 29 citations
• Causal Reasoning for Algorithmic Fairness (2018) — 147 citations
• Counterfactual Fairness (2017) — 1,918 citations
• When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness (2017) — 180 citations
• A significance test for forward stepwise model selection (2014) — 67 citations