Daniel J. Lizotte
Statistics and Probability · 2004–2021
A researcher in the Science corpus: 17 papers; trusts 98 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 96 citations into it; #2,806 of 12,932 by trust here, #1,282 by citations
- Reinforcement Learning — 79 citations into it; #1,998 of 2,242 by trust here, #1,055 by citations
- Artificial Intelligence — 58 citations into it; #9,769 of 35,345 by trust here, #12,761 by citations
- Computational Theory and Mathematics — 25 citations into it; #4,004 of 12,182 by trust here, #2,931 by citations
- Management Science and Operations Research — 22 citations into it; #3,644 of 11,946 by trust here, #3,047 by citations
- Mathematical Optimization — 17 citations into it
- Economics and Econometrics — 15 citations into it; #4,832 of 23,481 by trust here, #3,392 by citations
- Pharmacology — 15 citations into it
Whom Daniel J. Lizotte trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Susan A. Murphy
- Richard S. Sutton
- Dimitri P. Bertsekas
- Geoffrey J. Gordon
- Csaba Szepesvari
- Joelle Pineau
- Damien Ernst
- James M. Robins
- Andrew Barto
- Yufan Zhao
Who trusts Daniel J. Lizotte overall
- Eric B. Laber
- Ziyun Wang
- Nando de Freitas
- Bobak Shahriari
- Eric Brochu
- Peter I. Frazier
- Scott Kuindersma
- Kevin Swersky
- Jasper Snoek
- José Miguel Hernández-Lobato
Papers from Daniel J. Lizotte's perspective
Ordered by how strongly the researchers Daniel J. Lizotte 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)
- Gradient-based learning applied to document recognition
- 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
- Bayesian sparse sampling for on-line reward optimization (2005) — 126 citations
- An experimental methodology for response surface optimization methods (2011) — 59 citations
- Dual Representations for Dynamic Programming (2008) — 14 citations
- Active Model Selection (2004) — 79 citations
- Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010) — 223 citations
- Statistical Inference in Dynamic Treatment Regimes (2010) — 35 citations
- Set-valued dynamic treatment regimes for competing outcomes (2012) — 73 citations
- Dynamic treatment regimes: technical challenges and applications (2014) — 193 citations
- Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods (2021) — 676 citations
- Multi-Objective Markov Decision Processes for Data-Driven Decision Support (2016) — 64 citations
- Linear Fitted-Q Iteration with Multiple Reward Functions (2012) — 99 citations
- Convergent Fitted Value Iteration with Linear Function Approximation (2011) — 14 citations
- Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (2010) — 88 citations
- Assessing Confidence in Policies Learned from Sequential Randomized Trials (2009)
- Practical bayesian optimization (2008) — 320 citations
- Stable Dual Dynamic Programming (2007) — 35 citations
- Automatic Gait Optimization with Gaussian Process Regression (2007) — 341 citations