Jiantao Jiao
Artificial Intelligence · 2014–2023
A researcher in the Science corpus: 12 papers; trusts 114 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 211 citations into it; #9,414 of 35,345 by trust here, #4,656 by citations
- Reinforcement Learning — 93 citations into it
- Statistics and Probability — 85 citations into it; #5,788 of 12,932 by trust here, #3,330 by citations
- Safety, Robustness & Interpretability — 57 citations into it; #962 of 2,251 by trust here, #522 by citations
- Management Science and Operations Research — 55 citations into it
- Computer Vision and Pattern Recognition — 51 citations into it
- Statistical Learning Theory — 31 citations into it
- Control and Systems Engineering — 29 citations into it
Whom Jiantao Jiao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Gregory Valiant
- Paul Valiant
- Liam Paninski
- Yihong Wu
- Alexandre B. Tsybakov
- Yanjun Han
- Lucien Le Cam
- Tommaso Cai
- Jean Hausser
- Pengkun Yang
Who trusts Jiantao Jiao overall
- Jayadev Acharya
- Yanjun Han
- Yihong Wu
- Xiyang Liu
- Sewoong Oh
- Tsachy Weissman
- Ananda Theertha Suresh
- Pengkun Yang
- Weihao Gao
- Alon Orlitsky
Papers from Jiantao Jiao's perspective
Ordered by how strongly the researchers Jiantao Jiao trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Theoretically Principled Trade-off between Robustness and Accuracy (2019) — 3,092 citations
- Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism (2021) — 360 citations
- The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal (2017) — 48 citations
- Minimax Estimation of the $L_1$ Distance (2017) — 57 citations
- Maximum Likelihood Estimation of Functionals of Discrete Distributions (2014) — 112 citations
- Minimax Estimation of Discrete Distributions Under $\ell _{1}$ Loss (2014) — 48 citations
- Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons (2023) — 299 citations
- Minimax estimation of the L1 distance (2016) — 68 citations
- Minimax estimation of discrete distributions (2015) — 25 citations
- Minimax Rate-optimal Estimation of KL Divergence between Discrete Distributions (2016) — 44 citations
- Minimax Estimation of Functionals of Discrete Distributions (2014) — 269 citations
- Minimax Estimation of Discrete Distributions under ℓ1 Loss (2014) — 66 citations