The University of Chicago Booth School of Business

∙ Chicago , United States ∙ 0 followers

The University of Chicago Booth School of Business is the graduate business school of the University of Chicago in Chicago, Illinois.

Horseshoe Regularization for Machine Learning in Complex and Deep Models

Since the advent of the horseshoe priors for regularization, global-loca...
04/24/2019 ∙ by Anindya Bhadra, et al. ∙ 22 ∙ share

read it
Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acycli...
09/29/2019 ∙ by Xun Zheng, et al. ∙ 18 ∙ share

read it
Deep Fundamental Factor Models

Deep fundamental factor models are developed to interpret and capture no...
03/18/2019 ∙ by Matthew F. Dixon, et al. ∙ 12 ∙ share

read it
Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep ...
03/22/2019 ∙ by Yuexi Wang, et al. ∙ 4 ∙ share

read it
Sparse Regularization in Marketing and Economics

Sparse alpha-norm regularization has many data-rich applications in mark...
09/01/2017 ∙ by Guanhao Feng, et al. ∙ 0 ∙ share

read it
Deep Learning: A Bayesian Perspective

Deep learning is a form of machine learning for nonlinear high dimension...
06/01/2017 ∙ by Nicholas Polson, et al. ∙ 0 ∙ share

read it
Bayesian l_0 Regularized Least Squares

Bayesian l_0-regularized least squares provides a variable selection tec...
05/31/2017 ∙ by Nicholas G. Polson, et al. ∙ 0 ∙ share

read it
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...
04/18/2017 ∙ by Mickaël Binois, et al. ∙ 0 ∙ share

read it
A Bayesian optimization approach to find Nash equilibria

Game theory finds nowadays a broad range of applications in engineering ...
11/08/2016 ∙ by Victor Picheny, et al. ∙ 0 ∙ share

read it
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...
12/21/2017 ∙ by Tengyuan Liang, et al. ∙ 0 ∙ share

read it
Double/Debiased Machine Learning for Treatment and Causal Parameters

Most modern supervised statistical/machine learning (ML) methods are exp...
07/30/2016 ∙ by Victor Chernozhukov, et al. ∙ 0 ∙ share

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

An augmented Lagrangian (AL) can convert a constrained optimization prob...
05/31/2016 ∙ by Victor Picheny, et al. ∙ 0 ∙ share

read it
Distributed Multitask Learning

We consider the problem of distributed multi-task learning, where each m...
10/02/2015 ∙ by Jialei Wang, et al. ∙ 0 ∙ share

read it
A Statistical Theory of Deep Learning via Proximal Splitting

In this paper we develop a statistical theory and an implementation of d...
09/20/2015 ∙ by Nicholas G. Polson, et al. ∙ 0 ∙ share

read it
Proximal Algorithms in Statistics and Machine Learning

In this paper we develop proximal methods for statistical learning. Prox...
02/11/2015 ∙ by Nicholas G. Polson, et al. ∙ 0 ∙ share

read it
Sequential Design for Optimal Stopping Problems

We propose a new approach to solve optimal stopping problems via simulat...
09/16/2013 ∙ by Robert B. Gramacy, et al. ∙ 0 ∙ share

read it
Bayesian inference for logistic models using Polya-Gamma latent variables

We propose a new data-augmentation strategy for fully Bayesian inference...
05/02/2012 ∙ by Nicholas G. Polson, et al. ∙ 0 ∙ share

read it
Dynamic trees for streaming and massive data contexts

Data collection at a massive scale is becoming ubiquitous in a wide vari...
01/26/2012 ∙ by Christoforos Anagnostopoulos, et al. ∙ 0 ∙ share

read it
The Bayesian Bridge

We propose the Bayesian bridge estimator for regularized regression and ...
09/11/2011 ∙ by Nicholas G. Polson, et al. ∙ 0 ∙ share

read it
Exceedance as a measure of sparsity

Sparsity is defined as a limiting property of a sequence of probability ...
12/11/2017 ∙ by Peter McCullagh, et al. ∙ 0 ∙ share

read it
Replication or exploration? Sequential design for stochastic simulation experiments

In this paper we investigate the merits of replication, and provide meth...
10/09/2017 ∙ by Mickaël Binois, et al. ∙ 0 ∙ share

read it
Dynamic Mixed Frequency Synthesis for Economic Nowcasting

We develop a novel Bayesian framework for dynamic modeling of mixed freq...
12/11/2017 ∙ by Kenichiro McAlinn, et al. ∙ 0 ∙ share

read it
Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting

We develop the methodology and a detailed case study in use of a class o...
11/05/2017 ∙ by Kenichiro McAlinn, et al. ∙ 0 ∙ share

read it
Propensity score methodology in the presence of network entanglement between treatments

In experimental design and causal inference, it may happen that the trea...
01/22/2018 ∙ by Panos Toulis, et al. ∙ 0 ∙ share

read it
Low-rank Bandit Methods for High-dimensional Dynamic Pricing

We consider high dimensional dynamic multi-product pricing with an evolv...
01/30/2018 ∙ by Jonas Mueller, et al. ∙ 0 ∙ share

read it
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...
02/18/2018 ∙ by Belinda Tzen, et al. ∙ 0 ∙ share

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

We study in this paper the rate of convergence for learning densities un...
12/21/2017 ∙ by Tengyuan Liang, et al. ∙ 0 ∙ share

read it
Weighted Bayesian Bootstrap for Scalable Bayes

We develop a weighted Bayesian Bootstrap (WBB) for machine learning and ...
03/12/2018 ∙ by Michael Newton, et al. ∙ 0 ∙ share

read it
Deep Factor Alpha

Deep Factor Alpha provides a framework for extracting nonlinear factors ...
05/03/2018 ∙ by Guanhao Feng, et al. ∙ 0 ∙ share

read it
Learning Influence-Receptivity Network Structure with Guarantee

Traditional works on community detection from observations of informatio...
06/14/2018 ∙ by Ming Yu, et al. ∙ 0 ∙ share

read it
The Median Probability Model and Correlated Variables

The median probability model (MPM) Barbieri and Berger (2004) is defined...
07/22/2018 ∙ by Marilena Barbieri, et al. ∙ 0 ∙ share

read it
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize

In the absence of explicit regularization, Kernel "Ridgeless" Regression...
08/01/2018 ∙ by Tengyuan Liang, et al. ∙ 0 ∙ share

read it
Latent Agents in Networks: Estimation and Pricing

We focus on a setting where agents in a social network consume a product...
08/14/2018 ∙ by Baris Ata, et al. ∙ 0 ∙ share

read it
Bayesian Hypothesis Testing: Redux

Bayesian hypothesis testing is re-examined from the perspective of an a ...
08/26/2018 ∙ by Hedibert F. Lopes, et al. ∙ 0 ∙ share

read it
Characteristic-Sorted Portfolios: Estimation and Inference

Portfolio sorting is ubiquitous in the empirical finance literature, whe...
09/10/2018 ∙ by Matias D. Cattaneo, et al. ∙ 0 ∙ share

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

We study in this paper the rate of convergence for learning distribution...
11/07/2018 ∙ by Tengyuan Liang, et al. ∙ 0 ∙ share

read it
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...
01/21/2019 ∙ by Xialiang Dou, et al. ∙ 0 ∙ share

read it
Factor Investing: Hierarchical Ensemble Learning

We present a Bayesian hierarchical framework for both cross-sectional an...
02/04/2019 ∙ by Guanhao Feng, et al. ∙ 0 ∙ share

read it
Binscatter Regressions

We introduce the Stata (and R) package Binsreg, which implements the bin...
02/25/2019 ∙ by Matias D. Cattaneo, et al. ∙ 0 ∙ share

read it
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×...
03/01/2019 ∙ by Jiaming Xu, et al. ∙ 0 ∙ share

read it
Information and Memory in Dynamic Resource Allocation

We propose a general framework, dubbed Stochastic Processing under Imper...
04/17/2019 ∙ by Kuang Xu, et al. ∙ 0 ∙ share

read it
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Probabilistic graphical models provide a flexible yet parsimonious frame...
05/15/2019 ∙ by Ming Yu, et al. ∙ 0 ∙ share

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

Nonparametric kernel density and local polynomial regression estimators ...
06/01/2019 ∙ by Sebastian Calonico, et al. ∙ 0 ∙ share

read it
Racial Disparities in Voting Wait Times: Evidence from Smartphone Data

Equal access to voting is a core feature of democratic government. Using...
08/30/2019 ∙ by M. Keith Chen, et al. ∙ 0 ∙ share

read it
Regularized deep learning with a non-convex penalty

Regularization methods are often employed in deep learning neural networ...
09/11/2019 ∙ by Sujit Vettam, et al. ∙ 0 ∙ share

read it
Regularized deep learning with non-convex penalties

Regularization methods are often employed in deep learning neural networ...
09/11/2019 ∙ by Sujit Vettam, et al. ∙ 0 ∙ share

read it
Constrained High Dimensional Statistical Inference

In typical high dimensional statistical inference problems, confidence i...
11/17/2019 ∙ by Ming Yu, et al. ∙ 0 ∙ share

read it
Diagnostic Curves for Black Box Models

In safety-critical applications of machine learning, it is often necessa...
12/02/2019 ∙ by David I. Inouye, et al. ∙ 0 ∙ share

read it
Randomization Tests in Observational Studies with Staggered Adoption of Treatment

This paper studies inference in observational studies with time-varying ...
12/23/2019 ∙ by Azeem Shaikh, et al. ∙ 0 ∙ share

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

In this paper, we utilize a connection between compositional kernels and...
04/09/2020 ∙ by Tengyuan Liang, et al. ∙ 0 ∙ share

read it

The University of Chicago Booth School of Business

Featured Researchers