The Technological Elements of Artificial Intelligence.

Low-rank Bandit Methods for High-dimensional Dynamic Pricing with Mueller and Syrgkanis.

The Geometry of Culture: Analyzing Meaning through Word Embeddings with Kozlowski and Evans.

Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels with Chernozhukov, Goldman, and Semenova.

Accurate Inference for Adaptive Linear Models with Deshpande, Mackey, and Syrgkanis.

Text as Data with Gentzkow and Kelly.

Measuring group differences in high dimensional choices: Method and application to Congressional speech with Gentzkow and Shapiro, to appear in Econometrica.

Measuring Technological Innovation over the Long Run with Kelly, Papanikolaou, and Seru.

Counterfactual Prediction with Deep Instrumental Variables Networks with Hartford, Lewis, and Leyton-Brown in ICML 2017

Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs with Wan, Wang, Goldman, Rao, Liu, Lymberopoulos, McAuley in WWW 2017.

One-step estimator paths for concave regularization. Journal of Computational and Graphical Statistics 26, 2017. arXiv, supplemental appendix, and the gamlr R package.

Beyond Bilingual: Multi-Sense Word Embeddings Using Multilingual Context with Upadhyay, Chang, Kalai, and Zou in Repl4NLP 2017.

Comment: A regularization scheme on word occurrence rates that improves estimation and interpretation of topical content (Airoldi and Bischof). Journal of the American Statistical Association, 2017.

Comment: Coauthorship and citation networks for statisticians (Ji and Jin) with Kolar. Annals of Applied Statistics, 2017.

Scalable semi-parametric inference for the means of heavy-tailed distributions with Lopes and Gardner.

A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation with Gardner, Chen, and Draper. To appear in the Journal of Business and Economic Statistics. arXiv.

Document classification by inversion of distributed language representations. Proceedings of the 53rd meeting of the Association for Computational Linguistics (ACL 2015). gensim demo.

Bayesian and empirical Bayesian forests with Chen, Yu, and Wyle. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015). pyspark demo.

Distributed multinomial regression. Annals of Applied Statistics 9, 2015. arXiv, distrom R package, +textir for use in MNIR.

Causal inference in repeated observational studies: A case study of eBay product releases with Von Brzeski and Draper.

Hockey Player Performance via Regularized Logistic Regression with Gramacy and Tian. To appear in the Handbook of statistical methods for design and analysis in sports. slides

Multinomial inverse regression for text analysis, with discussion and rejoinder. Journal of the American Statistical Association 108, 2013. textir R package, arXiv paper and rejoinder.

Measuring political sentiment on Twitter: factor-optimal design for multinomial inverse regression. Technometrics 55, 2013. arXiv

Estimating Player Contribution in Hockey with Regularized Logistic Regression, with Gramacy and Jensen. Journal of Quantitative Analysis of Sports 9, 2013. arXiv, Booth piece, Chance article.

Variable Selection and Sensitivity Analysis via Dynamic Trees with an Application to Computer Code Performance Tuning, with Gramacy and Wild. Annals of Applied Statistics 7, 2013. arXiv). Argonne write-up.

On Estimation and Selection for Topic Models. AISTATS 2012, JMLR W&CP 22. maptpx R package, we8there.R example.

Mixture Modelling for Marked Poisson Processes, with Kottas. Bayesian Analysis 7, 2012.

Dynamic Trees for Learning and Design, with Gramacy and Polson. Journal of the American Statistical Association 106, 2011. arXiv, dynaTree R package

An auto-regressive mixture model for dynamic spatial Poisson processes: Application to tracking the intensity of violent crime. Journal of the American Statistical Association 105, 2010. local copy

Particle learning for general mixtures, with Carvalho, Lopes, and Polson. Bayesian Analysis 5, 2010.

A Bayesian nonparametric approach to inference for quantile regression, with Kottas. Journal of Business and Economic Statistics 28, 2010. (local copy)

Designing and anlayzing a circuit device experiment using treed Gaussian processes, with Lee, Gramacy, and Gray. A version of this appears as a chapter in the Handbook of Applied Bayesian Analysis, OUP 2010.

Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, with Gramacy. Journal of Statistical Software 33, 2010. R-vignette version

Selection of a representative sample, with Lee and Gray. Journal of Classification 27, 2010. local copy

Markov switching Dirichlet process mixture regression, with Kottas. Bayesian Analysis 4, 2009.

Bayesian guided pattern search for robust local optimization, with Lee, Gray, and Griffin. Technometrics 51, 2009. local copy

Fast inference for statistical inverse problems, with Lee and Sansó. Inverse Problems 25, 2009. local copy

A statistical framework for the sensitivity analysis of radiative transfer models, with Morris, Kottas, Furfaro, and Ganapol. IEEE Transactions on Geoscience and Remote Sensing 12, 2008. local copy

Thesis: Bayesian nonparametric analysis of conditional distributions and inference for Poisson point processes.