Alejandro Schuler

Statistics and Probability · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 22 citations into it; #11,126 of 12,932 by trust here, #8,279 by citations
• Artificial Intelligence — 20 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Causal Inference & Discovery — 8 citations into it
• Optimization & Training — 8 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Alejandro Schuler trusts overall

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

1. Donald B. Rubin
2. Peter C. Austin
3. Jerome H. Friedman
4. Radford M. Neal
5. Yarin Gal
6. Alex Graves
7. Susan Athey

Who trusts Alejandro Schuler overall

1. Ahmed M. Alaa

Papers from Alejandro Schuler's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Random Forests
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Practical Variational Inference for Neural Networks
8. Bayesian learning for neural networks
9. Long Short-Term Memory
10. ImageNet: A large-scale hierarchical image database

Their papers

• NGBoost: Natural Gradient Boosting for Probabilistic Prediction (2019) — 474 citations
• Some methods for heterogeneous treatment effect estimation in high dimensions (2017) — 244 citations