David Maxwell Chickering

Artificial Intelligence · 1994–2015

A researcher in the Science corpus: 9 papers; trusts 16 researchers and is trusted by 231 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #15,962 of 35,345 by trust here, #4,526 by citations
• Probabilistic & Bayesian Methods — 27 citations into it; #198 of 1,525 by trust here, #170 by citations
• Statistics and Probability — 21 citations into it
• Management Science and Operations Research — 18 citations into it; #4,674 of 11,946 by trust here, #510 by citations
• Reinforcement Learning — 12 citations into it; #1,576 of 2,242 by trust here, #591 by citations
• Causal Inference & Discovery — 6 citations into it

Whom David Maxwell Chickering trusts overall

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

1. Christopher Meek
2. David E. Heckerman
3. Dan Geiger
4. M. Chickering
5. Judea Pearl
6. Thomas Verma
7. Wray L. Buntine
8. Tom L. Burr
9. Julian Besag
10. Zoubin Ghahramani

Who trusts David Maxwell Chickering overall

1. Peter Buhlmann
2. Jan Peters
3. Alain Hauser
4. Frederick Eberhardt
5. Marloes H. Maathuis
6. J. Ramsey
7. Clark Glymour
8. Diviyan Kalainathan
9. Kun Zhang
10. Antti Hyttinen

Papers from David Maxwell Chickering's perspective

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

1. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
2. Probabilistic reasoning in intelligent systems: Networks of plausible inference
3. Probabilistic reasoning in intelligent systems: Networks of plausible inference
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
7. Reinforcement Learning: An Introduction
8. Attention is All you Need (Neural Information Processing Systems)
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Gradient-based learning applied to document recognition

Their papers

• Counterfactual reasoning and learning systems: the example of computational advertising (2012) — 859 citations
• ModelTracker: Redesigning Performance Analysis Tools for Machine Learning (2015) — 311 citations
• Large-Sample Learning of Bayesian Networks is NP-Hard (2002) — 851 citations
• ARMA Time-Series Modeling with Graphical Models (2004) — 20 citations
• Optimal Structure Identification With Greedy Search (2002) — 2,270 citations
• Dependency Networks for Inference, Collaborative Filtering, and Data Visualization (2000) — 643 citations
• Learning Mixtures of DAG Models (1998)
• Learning Equivalence Classes of Bayesian Network Structures (1996)
• Learning Bayesian networks: The combination of knowledge and statistical data (1994)