Po-Ling Loh
Statistics and Probability · 2011–2020
A researcher in the Science corpus: 6 papers; trusts 71 researchers and is trusted by 58 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 48 citations into it; #1,104 of 12,932 by trust here, #1,548 by citations
- Artificial Intelligence — 43 citations into it; #5,278 of 35,345 by trust here, #10,667 by citations
- Computational Mechanics — 29 citations into it; #1,074 of 10,001 by trust here, #2,309 by citations
- Statistical Learning Theory — 17 citations into it
- Optimization & Training — 16 citations into it; #1,161 of 2,601 by trust here, #1,571 by citations
- Computer Vision and Pattern Recognition — 9 citations into it
- Applied Mathematics — 5 citations into it
- Causal Inference & Discovery — 5 citations into it
Whom Po-Ling Loh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cun-Hui Zhang
- Martin J. Wainwright
- Jianqing Fan
- Peter Buhlmann
- Runze Li
- Michel Ledoux
- Hui Zou
- Ming Yuan
- Sara A. van de Geer
- Roman Vershynin
Who trusts Po-Ling Loh overall
- Yudong Chen
- Martin J. Wainwright
- Bryon Aragam
- John C. Duchi
- Jiashi Feng
- Stephen Tu
- DE Saravan
- Peter Buhlmann
- Fei Tang
- Xun Zheng
Papers from Po-Ling Loh's perspective
Ordered by how strongly the researchers Po-Ling Loh trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Regression Shrinkage and Selection via the Lasso
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity (2011) — 594 citations
- Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima (2013) — 564 citations
- Does Data Augmentation Lead to Positive Margin? (2019) — 38 citations
- Generalization Error Bounds for Noisy, Iterative Algorithms (2018) — 130 citations
- Book Review: High-dimensional statistics: A non-asymptotic viewpoint (2020) — 143 citations
- High-dimensional learning of linear causal networks via inverse covariance estimation (2013) — 216 citations