Antti Hyttinen

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 8 papers; trusts 54 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #17,454 of 35,345 by trust here, #13,544 by citations
• Probabilistic & Bayesian Methods — 26 citations into it
• Causal Inference & Discovery — 24 citations into it; #124 of 364 by trust here, #127 by citations
• Statistics and Probability — 24 citations into it
• Molecular Biology — 10 citations into it; #10,371 of 17,773 by trust here, #13,284 by citations
• Computer Networks and Communications — 7 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Antti Hyttinen trusts overall

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

1. Tom L. Burr
2. David Maxwell Chickering
3. Judea Pearl
4. Peter Spirtes
5. Ilya Shpitser
6. Dan Geiger
7. Frederick Eberhardt
8. Thomas S. Richardson
9. Gregory F. Cooper
10. Armin Biere

Who trusts Antti Hyttinen overall

1. Sara Magliacane
2. Joris M. Mooij
3. Sofia Triantafillou
4. Kari Rantanen
5. Stephan Bongers
6. Frederick Eberhardt
7. Matti Järvisalo
8. Tom Claassen
9. Ioannis Tsamardinos
10. Yuhao Wang

Papers from Antti Hyttinen's perspective

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

1. Causality : Models , Reasoning , and Inference
2. Causation, Prediction, and Search
3. ImageNet classification with deep convolutional neural networks
4. Causation, prediction, and search
5. Optimal Structure Identification With Greedy Search
6. Gradient-based learning applied to document recognition
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Causal diagrams for empirical research
10. Long Short-Term Memory

Their papers

• Towards Scalable Bayesian Learning of Causal DAGs (2020) — 46 citations
• Learning linear cyclic causal models with latent variables (2012) — 150 citations
• Experiment selection for causal discovery (2013) — 112 citations
• Causal Discovery from Subsampled Time Series Data by Constraint Optimization (2016) — 46 citations
• Learning Optimal Causal Graphs with Exact Search (2018) — 10 citations
• Do-calculus when the True Graph Is Unknown (2015) — 66 citations
• Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming (2014) — 151 citations
• Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure (2013) — 97 citations