Muhammad Bilal Zafar

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 13 papers; trusts 61 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it
• Safety, Robustness & Interpretability — 82 citations into it; #407 of 2,251 by trust here, #405 by citations
• Safety Research — 62 citations into it; #363 of 5,829 by trust here, #157 by citations
• AI Ethics, Fairness & Society — 60 citations into it; #40 of 494 by trust here, #14 by citations
• Sociology and Political Science — 59 citations into it; #2,541 of 22,756 by trust here, #1,256 by citations
• Information Systems — 44 citations into it
• Statistical and Nonlinear Physics — 39 citations into it
• Statistics and Probability — 17 citations into it; #2,487 of 12,932 by trust here, #795 by citations

Whom Muhammad Bilal Zafar trusts overall

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

1. Faisal Kamiran
2. Toon Calders
3. Radford M. Neal
4. Richard S. Zemel
5. Cynthia Dwork
6. Moritz Hardt
7. Michael Feldman
8. Toshihiro Kamishima
9. Alexandra Chouldechova
10. Salvatore Ruggieri

Who trusts Muhammad Bilal Zafar overall

1. Luca Oneto
2. Nina Grgic-Hlaca
3. Robert C. Williamson
4. Krishna P. Gummadi
5. Silvia Chiappa
6. Aditya Krishna Menon
7. Dana Pessach
8. Adrian Weller
9. Naman Goel
10. L. Elisa Celis

Papers from Muhammad Bilal Zafar's perspective

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

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

Their papers

• Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment (2016) — 1,325 citations
• Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017) — 236 citations
• Sampling Content from Online Social Networks (2015) — 44 citations
• Fairness Constraints: A Flexible Approach for Fair Classification (2019) — 366 citations
• Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning (2018) — 243 citations
• A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices (2018) — 300 citations
• On Fairness, Diversity and Randomness in Algorithmic Decision Making (2017) — 44 citations
• From Parity to Preference-based Notions of Fairness in Classification (2017) — 225 citations
• The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making (2016) — 237 citations
• Learning Fair Classifiers (2015) — 50 citations
• Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015) — 46 citations
• Fairness Constraints: Mechanisms for Fair Classification (2015) — 1,360 citations
• Deep Twitter diving: exploring topical groups in microblogs at scale (2014) — 42 citations