Jun Sakuma

Artificial Intelligence · 2011–2018

A researcher in the Science corpus: 8 papers; trusts 71 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #3,426 of 35,345 by trust here, #3,907 by citations
• AI Ethics, Fairness & Society — 23 citations into it; #66 of 494 by trust here, #63 by citations
• Privacy & Security — 19 citations into it
• Statistics and Probability — 19 citations into it
• Sociology and Political Science — 17 citations into it; #2,577 of 22,756 by trust here, #2,108 by citations
• Information Systems — 16 citations into it
• Safety, Robustness & Interpretability — 15 citations into it; #524 of 2,251 by trust here, #570 by citations
• Management Science and Operations Research — 10 citations into it

Whom Jun Sakuma trusts overall

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

1. Toon Calders
2. Cynthia Dwork
3. Toshihiro Kamishima
4. Salvatore Ruggieri
5. Dino Pedreschi
6. Faisal Kamiran
7. Sicco Verwer
8. Moritz Hardt
9. Richard S. Sutton
10. Alexander Strehl

Who trusts Jun Sakuma overall

1. Indrė Žliobaitė
2. Lu Zhang
3. Luca Oneto
4. Muhammad Bilal Zafar
5. Benjamin Fish
6. Michael Feldman
7. Xintao Wu
8. Yongkai Wu
9. Toshihiro Kamishima
10. Aditya Krishna Menon

Papers from Jun Sakuma's perspective

Ordered by how strongly the researchers Jun Sakuma 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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-aware Learning through Regularization Approach (2011) — 464 citations
• Fairness-Aware Classifier with Prejudice Remover Regularizer (2012) — 896 citations
• Toward Distribution Estimation under Local Differential Privacy with Small Samples (2018) — 34 citations
• Differentially Private Chi-squared Test by Unit Circle Mechanism (2017) — 23 citations
• Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences (2015)
• Privacy-Preserving Statistical Analysis by Exact Logistic Regression (2015) — 25 citations
• Efficiency Improvement of Neutrality-Enhanced Recommendation (2013) — 41 citations
• Prediction with Model-Based Neutrality (2013) — 41 citations