Dominik Rothenhausler

Statistics and Probability · with Buhlmann · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 40 citations into it; #12,598 of 12,932 by trust here, #5,857 by citations
• Artificial Intelligence — 35 citations into it; #34,132 of 35,345 by trust here, #21,878 by citations
• Causal Inference & Discovery — 30 citations into it; #356 of 364 by trust here, #266 by citations
• Probabilistic & Bayesian Methods — 12 citations into it
• Computational Mechanics — 5 citations into it
• Control and Systems Engineering — 5 citations into it
• Statistical Learning Theory — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Dominik Rothenhausler trusts overall

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

1. Peter Buhlmann
2. Sara A. van de Geer
3. Jan Peters
4. Robert Tibshirani
5. Kenneth A. Bollen
6. Nicolai Meinshausen
7. Michael P. Murray
8. Donald B. Rubin
9. Christopher Meek
10. James M. Robins

Who trusts Dominik Rothenhausler overall

1. Adarsh Subbaswamy
2. Xun Zheng
3. Harvineet Singh
4. Nicolai Meinshausen
5. Peter F. Schulam
6. Chen Dan
7. Rina Singh

Papers from Dominik Rothenhausler's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Causality : Models , Reasoning , and Inference
5. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Gradient-based learning applied to document recognition
8. Optimal Structure Identification With Greedy Search
9. Long Short-Term Memory
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Anchor regression: Heterogeneous data meet causality (2018) — 266 citations
• Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions (2017) — 45 citations
• Causal inference in partially linear structural equation models (2016) — 34 citations