Yongkai Wu

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 10 papers; trusts 67 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #6,893 of 35,345 by trust here, #13,353 by citations
• AI Ethics, Fairness & Society — 82 citations into it; #56 of 494 by trust here, #133 by citations
• Safety Research — 73 citations into it; #766 of 5,829 by trust here, #1,540 by citations
• Safety, Robustness & Interpretability — 52 citations into it
• Causal Inference & Discovery — 49 citations into it; #108 of 364 by trust here, #127 by citations
• Sociology and Political Science — 39 citations into it; #7,313 of 22,756 by trust here, #16,952 by citations
• Statistics and Probability — 33 citations into it; #11,805 of 12,932 by trust here, #11,487 by citations
• Probabilistic & Bayesian Methods — 21 citations into it

Whom Yongkai Wu trusts overall

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

1. Toon Calders
2. Faisal Kamiran
3. Lu Zhang
4. Judea Pearl
5. Salvatore Ruggieri
6. Ilya Shpitser
7. Sara Hajian
8. Dino Pedreschi
9. Xintao Wu
10. Chen Avin

Who trusts Yongkai Wu overall

1. Lu Zhang
2. Xintao Wu
3. Ninareh Mehrabi
4. Luca Oneto
5. Silvia Chiappa
6. Fred Morstatter
7. Depeng Xu
8. Nripsuta Ani Saxena
9. Razieh Nabi
10. Kristina Lerman

Papers from Yongkai Wu's perspective

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

1. Causality : Models , Reasoning , and Inference
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Et al
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Discrimination-aware data mining
7. Gradient-based learning applied to document recognition
8. Fairness through awareness
9. A multidisciplinary survey on discrimination analysis
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Causal Framework for Discovering and Removing Direct and Indirect Discrimination (2016) — 191 citations
• Using Loglinear Model for Discrimination Discovery and Prevention (2016) — 16 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