Lu Zhang

Artificial Intelligence · with Wu · 2000–2019

A researcher in the Science corpus: 13 papers; trusts 81 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #5,331 of 35,345 by trust here, #9,092 by citations
• AI Ethics, Fairness & Society — 92 citations into it; #53 of 494 by trust here, #84 by citations
• Safety Research — 92 citations into it; #492 of 5,829 by trust here, #964 by citations
• Safety, Robustness & Interpretability — 70 citations into it
• Causal Inference & Discovery — 55 citations into it; #96 of 364 by trust here, #103 by citations
• Sociology and Political Science — 46 citations into it; #9,516 of 22,756 by trust here, #16,952 by citations
• Statistics and Probability — 33 citations into it; #11,806 of 12,932 by trust here, #11,487 by citations
• Probabilistic & Bayesian Methods — 23 citations into it

Whom Lu Zhang trusts overall

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

1. Toon Calders
2. Faisal Kamiran
3. Yongkai Wu
4. Judea Pearl
5. Salvatore Ruggieri
6. Sara Hajian
7. Dino Pedreschi
8. Xintao Wu
9. Ilya Shpitser
10. Peter Spirtes

Who trusts Lu Zhang overall

1. Yongkai Wu
2. Xintao Wu
3. Silvia Chiappa
4. Ninareh Mehrabi
5. Luca Oneto
6. Fred Morstatter
7. Razieh Nabi
8. Bilal Qureshi
9. Nripsuta Ani Saxena
10. Tom Everitt

Papers from Lu Zhang's perspective

Ordered by how strongly the researchers Lu Zhang 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. Et al
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Causality : Models , Reasoning , and Inference
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• FairGAN: Fairness-aware Generative Adversarial Networks (2018) — 379 citations
• Anti-discrimination learning: a causal modeling-based framework (2017) — 54 citations
• A Causal Framework for Discovering and Removing Direct and Indirect Discrimination (2016) — 191 citations
• Sustained photobiological hydrogen gas production upon reversible inactivation of oxygen evolution in the green alga Chlamydomonas reinhardtii. (2000) — 1,073 citations
• Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019) — 54 citations
• PC-Fairness: A Unified Framework for Measuring Causality-based Fairness (2019) — 130 citations
• Counterfactual Fairness: Unidentification, Bound and Algorithm (2019) — 115 citations
• Fairness-aware Classification: Criterion, Convexity, and Bounds (2018) — 28 citations
• Achieving non-discrimination in prediction (2017) — 33 citations
• On Discrimination Discovery Using Causal Networks (2016) — 22 citations
• Achieving Non-Discrimination in Data Release (2016) — 74 citations
• Situation Testing-Based Discrimination Discovery: A Causal Inference Approach (2016) — 60 citations
• A numerical examination of shear banding and simple shear non-coaxial flow rules (2006) — 160 citations