Lucas Janson

Statistics and Probability · 2013–2016

A researcher in the Science corpus: 5 papers; trusts 47 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 56 citations into it; #8,000 of 12,932 by trust here, #7,914 by citations
• Artificial Intelligence — 22 citations into it; #25,664 of 35,345 by trust here, #23,077 by citations
• Management Science and Operations Research — 21 citations into it; #5,896 of 11,946 by trust here, #4,746 by citations
• Reinforcement Learning — 20 citations into it; #1,722 of 2,242 by trust here, #1,822 by citations
• Genetics — 10 citations into it
• Causal Inference & Discovery — 7 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Molecular Biology — 7 citations into it; #11,087 of 17,773 by trust here, #16,150 by citations

Whom Lucas Janson trusts overall

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

1. Yoav Benjamini
2. Christopher R. Genovese
3. Joseph P. Romano
4. Dimitri P. Bertsekas
5. Jason D. Lee
6. Steven M. LaValle
7. Larry A. Wasserman
8. Ronald J. Williams
9. E.J. Candes
10. Bradley Efron

Who trusts Lucas Janson overall

1. E.J. Candes
2. Brian Ichter
3. Yingying Fan
4. Nikolaos Ignatiadis
5. Thomas B. Berrett
6. Yinlam Chow
7. Pragya Sur
8. James Harrison
9. Shangding Gu
10. Daniel Garcia Rasines

Papers from Lucas Janson's perspective

Ordered by how strongly the researchers Lucas Janson trusts endorse each paper. Same corpus, a different order for every researcher.

1. Controlling the false discovery rate: a practical and powerful approach to multiple testing
2. ImageNet classification with deep convolutional neural networks
3. Regression Shrinkage and Selection via the Lasso
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. © Institute of Mathematical Statistics, 2004 LEAST ANGLE REGRESSION
9. Policy Gradient Methods for Reinforcement Learning with Function Approximation
10. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY

Their papers

• Risk-Constrained Reinforcement Learning with Percentile Risk Criteria (2015) — 660 citations
• Familywise error rate control via knockoffs (2015) — 51 citations
• Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection (2016) — 553 citations
• Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection (2016) — 304 citations
• Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions (2013) — 625 citations