Alain Hauser

Artificial Intelligence · 2011–2016

A researcher in the Science corpus: 4 papers; trusts 59 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #3,695 of 35,345 by trust here, #10,798 by citations
• Statistics and Probability — 41 citations into it; #2,474 of 12,932 by trust here, #4,517 by citations
• Causal Inference & Discovery — 40 citations into it; #70 of 364 by trust here, #144 by citations
• Probabilistic & Bayesian Methods — 23 citations into it
• Molecular Biology — 17 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Information Systems — 9 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Alain Hauser trusts overall

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

1. David Maxwell Chickering
2. Markus Kalisch
3. Peter Buhlmann
4. Judea Pearl
5. Jan Peters
6. Gregory F. Cooper
7. Peter Spirtes
8. Daniel Eaton
9. Ioannis Tsamardinos
10. Frederick Eberhardt

Who trusts Alain Hauser overall

1. Peter Buhlmann
2. Jan Peters
3. Yuhao Wang
4. Karren D. Yang
5. Christina Heinze-Deml
6. Nicolai Meinshausen
7. Caroline Uhler
8. Marloes H. Maathuis
9. Mathias Drton
10. Joris M. Mooij

Papers from Alain Hauser's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Causality : Models , Reasoning , and Inference
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Regression Shrinkage and Selection via the Lasso
7. Optimal Structure Identification With Greedy Search
8. Equivalence and Synthesis of Causal Models
9. Probabilistic reasoning in intelligent systems: Networks of plausible inference
10. Causation, Prediction, and Search

Their papers

• Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs (2011) — 518 citations
• Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs (2013) — 106 citations
• High-dimensional consistency in score-based and hybrid structure learning (2015) — 151 citations
• UvA-DARE (Digital Academic Repository) Methods for causal inference from gene perturbation experiments and validation (2016)