The University of Chicago Booth School of Business

research

∙ 04/24/2019

Horseshoe Regularization for Machine Learning in Complex and Deep Models

Since the advent of the horseshoe priors for regularization, global-loca...

Anindya Bhadra, et al. ∙

research

∙ 09/29/2019

Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acycli...

Xun Zheng, et al. ∙

research

∙ 03/18/2019

Deep Fundamental Factor Models

Deep fundamental factor models are developed to interpret and capture no...

Matthew F. Dixon, et al. ∙

research

∙ 03/22/2019

Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep ...

Yuexi Wang, et al. ∙

research

∙ 09/01/2017

Sparse Regularization in Marketing and Economics

Sparse alpha-norm regularization has many data-rich applications in mark...

Guanhao Feng, et al. ∙

research

∙ 06/01/2017

Deep Learning: A Bayesian Perspective

Deep learning is a form of machine learning for nonlinear high dimension...

Nicholas Polson, et al. ∙

research

∙ 05/31/2017

Bayesian l_0 Regularized Least Squares

Bayesian l_0-regularized least squares provides a variable selection tec...

Nicholas G. Polson, et al. ∙

research

∙ 04/18/2017

On the choice of the low-dimensional domain for global optimization via random embeddings

The challenge of taking many variables into account in optimization prob...

Mickaël Binois, et al. ∙

research

∙ 11/08/2016

A Bayesian optimization approach to find Nash equilibria

Game theory finds nowadays a broad range of applications in engineering ...

Victor Picheny, et al. ∙

research

∙ 12/21/2017

How Well Can Generative Adversarial Networks (GAN) Learn Densities: A Nonparametric View

We study in this paper the rate of convergence for learning densities un...

Tengyuan Liang, et al. ∙

research

∙ 07/30/2016

Double/Debiased Machine Learning for Treatment and Causal Parameters

Most modern supervised statistical/machine learning (ML) methods are exp...

Victor Chernozhukov, et al. ∙

research

∙ 05/31/2016

Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian

An augmented Lagrangian (AL) can convert a constrained optimization prob...

Victor Picheny, et al. ∙

research

∙ 10/02/2015

Distributed Multitask Learning

We consider the problem of distributed multi-task learning, where each m...

Jialei Wang, et al. ∙

research

∙ 09/20/2015

A Statistical Theory of Deep Learning via Proximal Splitting

In this paper we develop a statistical theory and an implementation of d...

Nicholas G. Polson, et al. ∙

research

∙ 02/11/2015

Proximal Algorithms in Statistics and Machine Learning

In this paper we develop proximal methods for statistical learning. Prox...

Nicholas G. Polson, et al. ∙

research

∙ 09/16/2013

Sequential Design for Optimal Stopping Problems

We propose a new approach to solve optimal stopping problems via simulat...

Robert B. Gramacy, et al. ∙

research

∙ 05/02/2012

Bayesian inference for logistic models using Polya-Gamma latent variables

We propose a new data-augmentation strategy for fully Bayesian inference...

Nicholas G. Polson, et al. ∙

research

∙ 01/26/2012

Dynamic trees for streaming and massive data contexts

Data collection at a massive scale is becoming ubiquitous in a wide vari...

Christoforos Anagnostopoulos, et al. ∙

research

∙ 09/11/2011

The Bayesian Bridge

We propose the Bayesian bridge estimator for regularized regression and ...

Nicholas G. Polson, et al. ∙

research

∙ 12/11/2017

Exceedance as a measure of sparsity

Sparsity is defined as a limiting property of a sequence of probability ...

Peter McCullagh, et al. ∙

research

∙ 10/09/2017

Replication or exploration? Sequential design for stochastic simulation experiments

In this paper we investigate the merits of replication, and provide meth...

Mickaël Binois, et al. ∙

research

∙ 12/11/2017

Dynamic Mixed Frequency Synthesis for Economic Nowcasting

We develop a novel Bayesian framework for dynamic modeling of mixed freq...

Kenichiro McAlinn, et al. ∙

research

∙ 11/05/2017

Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting

We develop the methodology and a detailed case study in use of a class o...

Kenichiro McAlinn, et al. ∙

research

∙ 01/22/2018

Propensity score methodology in the presence of network entanglement between treatments

In experimental design and causal inference, it may happen that the trea...

Panos Toulis, et al. ∙

research

∙ 01/30/2018

Low-rank Bandit Methods for High-dimensional Dynamic Pricing

We consider high dimensional dynamic multi-product pricing with an evolv...

Jonas Mueller, et al. ∙

research

∙ 02/18/2018

Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability

We study the detailed path-wise behavior of the discrete-time Langevin a...

Belinda Tzen, et al. ∙

research

∙ 12/21/2017

How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View

We study in this paper the rate of convergence for learning densities un...

Tengyuan Liang, et al. ∙

research

∙ 03/12/2018

Weighted Bayesian Bootstrap for Scalable Bayes

We develop a weighted Bayesian Bootstrap (WBB) for machine learning and ...

Michael Newton, et al. ∙

research

∙ 05/03/2018

Deep Factor Alpha

Deep Factor Alpha provides a framework for extracting nonlinear factors ...

Guanhao Feng, et al. ∙

research

∙ 06/14/2018

Learning Influence-Receptivity Network Structure with Guarantee

Traditional works on community detection from observations of informatio...

Ming Yu, et al. ∙

research

∙ 07/22/2018

The Median Probability Model and Correlated Variables

The median probability model (MPM) Barbieri and Berger (2004) is defined...

Marilena Barbieri, et al. ∙

research

∙ 08/01/2018

Just Interpolate: Kernel "Ridgeless" Regression Can Generalize

In the absence of explicit regularization, Kernel "Ridgeless" Regression...

Tengyuan Liang, et al. ∙

research

∙ 08/14/2018

Latent Agents in Networks: Estimation and Pricing

We focus on a setting where agents in a social network consume a product...

Baris Ata, et al. ∙

research

∙ 08/26/2018

Bayesian Hypothesis Testing: Redux

Bayesian hypothesis testing is re-examined from the perspective of an a ...

Hedibert F. Lopes, et al. ∙

research

∙ 09/10/2018

Characteristic-Sorted Portfolios: Estimation and Inference

Portfolio sorting is ubiquitous in the empirical finance literature, whe...

Matias D. Cattaneo, et al. ∙

research

∙ 11/07/2018

On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results

We study in this paper the rate of convergence for learning distribution...

Tengyuan Liang, et al. ∙

research

∙ 01/21/2019

Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits

Consider the problem: given data pair (x, y) drawn from a population wit...

Xialiang Dou, et al. ∙

research

∙ 02/04/2019

Factor Investing: Hierarchical Ensemble Learning

We present a Bayesian hierarchical framework for both cross-sectional an...

Guanhao Feng, et al. ∙

research

∙ 02/25/2019

Binscatter Regressions

We introduce the Stata (and R) package Binsreg, which implements the bin...

Matias D. Cattaneo, et al. ∙

research

∙ 03/01/2019

Improved queue-size scaling for input-queued switches via graph factorization

This paper studies the scaling of the expected total queue size in an n×...

Jiaming Xu, et al. ∙

research

∙ 04/17/2019

Information and Memory in Dynamic Resource Allocation

We propose a general framework, dubbed Stochastic Processing under Imper...

Kuang Xu, et al. ∙

research

∙ 05/15/2019

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Probabilistic graphical models provide a flexible yet parsimonious frame...

Ming Yu, et al. ∙

research

∙ 06/01/2019

nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference

Nonparametric kernel density and local polynomial regression estimators ...

Sebastian Calonico, et al. ∙

research

∙ 08/30/2019

Racial Disparities in Voting Wait Times: Evidence from Smartphone Data

Equal access to voting is a core feature of democratic government. Using...

M. Keith Chen, et al. ∙

research

∙ 09/11/2019

Regularized deep learning with a non-convex penalty

Regularization methods are often employed in deep learning neural networ...

Sujit Vettam, et al. ∙

research

∙ 09/11/2019

Regularized deep learning with non-convex penalties

Regularization methods are often employed in deep learning neural networ...

Sujit Vettam, et al. ∙

research

∙ 11/17/2019

Constrained High Dimensional Statistical Inference

In typical high dimensional statistical inference problems, confidence i...

Ming Yu, et al. ∙

research

∙ 12/02/2019

Diagnostic Curves for Black Box Models

In safety-critical applications of machine learning, it is often necessa...

David I. Inouye, et al. ∙

research

∙ 12/23/2019

Randomization Tests in Observational Studies with Staggered Adoption of Treatment

This paper studies inference in observational studies with time-varying ...

Azeem Shaikh, et al. ∙

research

∙ 04/09/2020

Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks

In this paper, we utilize a connection between compositional kernels and...

Tengyuan Liang, et al. ∙

The University of Chicago Booth School of Business

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