Matt Taddy

Statistics and Probability · 2010–2018

A researcher in the Science corpus: 16 papers; trusts 240 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 188 citations into it; #2,030 of 12,932 by trust here, #3,411 by citations
• Artificial Intelligence — 177 citations into it; #6,389 of 35,345 by trust here, #11,624 by citations
• Economics and Econometrics — 65 citations into it
• Natural Language Processing — 55 citations into it
• Management Science and Operations Research — 51 citations into it; #6,795 of 11,946 by trust here, #5,316 by citations
• Representation & Self-Supervised Learning — 32 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Sociology and Political Science — 28 citations into it; #8,857 of 22,756 by trust here, #8,212 by citations

Whom Matt Taddy trusts overall

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

1. Athanasios Kottas
2. Radford M. Neal
3. Robert Tibshirani
4. Hemant Ishwaran
5. Victor Chernozhukov
6. Leo Breiman
7. Gary Chamberlain
8. Hugh A. Chipman
9. S. MacEachern
10. A. Brix

Who trusts Matt Taddy overall

1. Thomas J. Leininger
2. Thomas Kneib
3. Matthew A. Taddy
4. Federico Bassetti
5. Susan Athey
6. Athanasios Kottas
7. Surya T. Tokdar
8. Alan E. Gelfand
9. C. Ertekin
10. Alex Peysakhovich

Papers from Matt Taddy's perspective

Ordered by how strongly the researchers Matt Taddy 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Counterfactual Prediction with Deep Instrumental Variables Networks (2016) — 48 citations
• Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context (2017) — 27 citations
• Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2011) — 37 citations
• Bayesian and Empirical Bayesian Forests (2015) — 35 citations
• Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime (2010) — 94 citations
• Mixture Modeling for Marked Poisson Processes (2010) — 71 citations
• A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010) — 139 citations
• Text as Data (2017) — 1,062 citations
• Heterogeneous Treatment Effects in Digital Experimentation (2014) — 67 citations
• Accurate Inference for Adaptive Linear Models (2017) — 72 citations
• Measuring Technological Innovation over the Long Run (2018) — 355 citations
• The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings (2018) — 516 citations
• Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels (2017) — 50 citations
• Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017) — 21 citations
• Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017) — 63 citations
• Deep IV: A Flexible Approach for Counterfactual Prediction (2017) — 362 citations