Caroline Uhler

Artificial Intelligence · 2009–2019

A researcher in the Science corpus: 10 papers; trusts 84 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #5,417 of 35,345 by trust here, #8,999 by citations
• Statistics and Probability — 51 citations into it; #5,111 of 12,932 by trust here, #4,809 by citations
• Causal Inference & Discovery — 38 citations into it; #142 of 364 by trust here, #198 by citations
• Probabilistic & Bayesian Methods — 26 citations into it
• Computational Theory and Mathematics — 20 citations into it; #5,693 of 12,182 by trust here, #7,851 by citations
• Molecular Biology — 16 citations into it; #2,050 of 17,773 by trust here, #8,381 by citations
• Economics and Econometrics — 8 citations into it
• Applied Mathematics — 7 citations into it

Whom Caroline Uhler trusts overall

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

1. David Maxwell Chickering
2. Peter Spirtes
3. Tom L. Burr
4. Alain Hauser
5. Olivier Ledoit
6. Peter Buhlmann
7. J. Gerring
8. Mee Young Park
9. Frederick Eberhardt
10. Kamalika Chaudhuri

Who trusts Caroline Uhler overall

1. Daniel Kifer
2. Yue Wang
3. Joris M. Mooij
4. Sean K. Simmons
5. Kazuya Kakizaki
6. Jordan Awan
7. Jing Lei
8. Sara A. van de Geer
9. DE Saravan
10. Kamalika Chaudhuri

Papers from Caroline Uhler's perspective

Ordered by how strongly the researchers Caroline Uhler 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. Regression Shrinkage and Selection via the Lasso
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Causation, Prediction, and Search
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Equivalence and Synthesis of Causal Models

Their papers

• Maximum likelihood estimation for linear Gaussian covariance models (2014) — 64 citations
• Permutation-Based Causal Structure Learning with Unknown Intervention Targets (2019) — 110 citations
• Geometry of the faithfulness assumption in causal inference (2012) — 260 citations
• Total positivity in Markov structures (2015) — 80 citations
• Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (2018) — 135 citations
• Privacy-Preserving Data Sharing for Genome-Wide Association Studies (2012) — 159 citations
• Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry (2009) — 96 citations
• Privacy Preserving GWAS Data Sharing (2011) — 103 citations
• Permutation-based Causal Inference Algorithms with Interventions (2017) — 169 citations
• Covariance Matrix Estimation under Total Positivity for Portfolio Selection* (2019) — 47 citations