Eric B. Laber
Statistics and Probability · 2009–2020
A researcher in the Science corpus: 27 papers; trusts 173 researchers and is trusted by 51 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 342 citations into it; #578 of 12,932 by trust here, #353 by citations
- Artificial Intelligence — 92 citations into it; #8,875 of 35,345 by trust here, #15,542 by citations
- Reinforcement Learning — 87 citations into it; #2,075 of 2,242 by trust here, #1,055 by citations
- Causal Inference & Discovery — 69 citations into it; #277 of 364 by trust here, #311 by citations
- Economics and Econometrics — 42 citations into it; #3,021 of 23,481 by trust here, #2,831 by citations
- Experimental and Cognitive Psychology — 37 citations into it; #4,688 of 9,731 by trust here, #7,727 by citations
- Management Science and Operations Research — 37 citations into it; #2,685 of 11,946 by trust here, #3,047 by citations
- Pharmacology — 36 citations into it
Whom Eric B. Laber trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Susan A. Murphy
- James M. Robins
- Baqun Zhang
- Donald B. Rubin
- Anastasios A. Tsiatis
- Philip W. Lavori
- Bibhas Chakraborty
- Ying‐Qi Zhao
- Daniel J. Lizotte
- Yufan Zhao
Who trusts Eric B. Laber overall
- Michael R. Kosorok
- Kristin A. Linn
- Ying‐Qi Zhao
- Chao Yu
- Jesse Clifton
- Daniel J. Lizotte
- Yichi Zhang
- Xinkun Nie
- Leonard A. Stefanski
- Jiming Liu
Papers from Eric B. Laber's perspective
Ordered by how strongly the researchers Eric B. Laber trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Reinforcement Learning: An Introduction
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Regression Shrinkage and Selection via the Lasso
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
Their papers
- A Robust Method for Estimating Optimal Treatment Regimes (2012) — 423 citations
- Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions (2013) — 226 citations
- New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes (2015) — 263 citations
- Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010) — 223 citations
- Estimating Optimal Treatment Regimes from a Classification Perspective (2012) — 279 citations
- Interactive model building for Q-learning (2014) — 78 citations
- Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes (2012) — 240 citations
- Doubly Robust Learning for Estimating Individualized Treatment with Censored Data (2014) — 156 citations
- Statistical Inference in Dynamic Treatment Regimes (2010) — 35 citations
- Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015) — 108 citations
- Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m‐Out‐of‐n Bootstrap Scheme (2013) — 131 citations
- Dynamic Treatment Regimes (2019) — 73 citations
- Q-Learning: Theory and Applications (2020) — 460 citations
- Precision Medicine. (2019) — 237 citations
- Set-valued dynamic treatment regimes for competing outcomes (2012) — 73 citations
- Dynamic treatment regimes: technical challenges and applications (2014) — 193 citations
- Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning (2016) — 164 citations
- Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules (2017) — 40 citations
- Interactive Q-learning for Probabilities and Quantiles (2014) — 7 citations
- Interactive Q-learning for Quantiles (2017) — 47 citations