
Horseshoe Regularization for Machine Learning in Complex and Deep Models
Since the advent of the horseshoe priors for regularization, globalloca...
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Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acycli...
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Deep Fundamental Factor Models
Deep fundamental factor models are developed to interpret and capture no...
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Scalable Data Augmentation for Deep Learning
Scalable Data Augmentation (SDA) provides a framework for training deep ...
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Sparse Regularization in Marketing and Economics
Sparse alphanorm regularization has many datarich applications in mark...
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Deep Learning: A Bayesian Perspective
Deep learning is a form of machine learning for nonlinear high dimension...
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Bayesian l_0 Regularized Least Squares
Bayesian l_0regularized least squares provides a variable selection tec...
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On the choice of the lowdimensional domain for global optimization via random embeddings
The challenge of taking many variables into account in optimization prob...
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A Bayesian optimization approach to find Nash equilibria
Game theory finds nowadays a broad range of applications in engineering ...
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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...
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Double/Debiased Machine Learning for Treatment and Causal Parameters
Most modern supervised statistical/machine learning (ML) methods are exp...
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Bayesian optimization under mixed constraints with a slackvariable augmented Lagrangian
An augmented Lagrangian (AL) can convert a constrained optimization prob...
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Distributed Multitask Learning
We consider the problem of distributed multitask learning, where each m...
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A Statistical Theory of Deep Learning via Proximal Splitting
In this paper we develop a statistical theory and an implementation of d...
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Proximal Algorithms in Statistics and Machine Learning
In this paper we develop proximal methods for statistical learning. Prox...
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Sequential Design for Optimal Stopping Problems
We propose a new approach to solve optimal stopping problems via simulat...
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Bayesian inference for logistic models using PolyaGamma latent variables
We propose a new dataaugmentation strategy for fully Bayesian inference...
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Dynamic trees for streaming and massive data contexts
Data collection at a massive scale is becoming ubiquitous in a wide vari...
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The Bayesian Bridge
We propose the Bayesian bridge estimator for regularized regression and ...
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Exceedance as a measure of sparsity
Sparsity is defined as a limiting property of a sequence of probability ...
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Replication or exploration? Sequential design for stochastic simulation experiments
In this paper we investigate the merits of replication, and provide meth...
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Dynamic Mixed Frequency Synthesis for Economic Nowcasting
We develop a novel Bayesian framework for dynamic modeling of mixed freq...
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Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting
We develop the methodology and a detailed case study in use of a class o...
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Propensity score methodology in the presence of network entanglement between treatments
In experimental design and causal inference, it may happen that the trea...
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Lowrank Bandit Methods for Highdimensional Dynamic Pricing
We consider high dimensional dynamic multiproduct pricing with an evolv...
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Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability
We study the detailed pathwise behavior of the discretetime Langevin a...
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How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View
We study in this paper the rate of convergence for learning densities un...
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Weighted Bayesian Bootstrap for Scalable Bayes
We develop a weighted Bayesian Bootstrap (WBB) for machine learning and ...
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Deep Factor Alpha
Deep Factor Alpha provides a framework for extracting nonlinear factors ...
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Learning InfluenceReceptivity Network Structure with Guarantee
Traditional works on community detection from observations of informatio...
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The Median Probability Model and Correlated Variables
The median probability model (MPM) Barbieri and Berger (2004) is defined...
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Just Interpolate: Kernel "Ridgeless" Regression Can Generalize
In the absence of explicit regularization, Kernel "Ridgeless" Regression...
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Latent Agents in Networks: Estimation and Pricing
We focus on a setting where agents in a social network consume a product...
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Bayesian Hypothesis Testing: Redux
Bayesian hypothesis testing is reexamined from the perspective of an a ...
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CharacteristicSorted Portfolios: Estimation and Inference
Portfolio sorting is ubiquitous in the empirical finance literature, whe...
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On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results
We study in this paper the rate of convergence for learning distribution...
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Training Neural Networks as Learning Dataadaptive Kernels: Provable Representation and Approximation Benefits
Consider the problem: given data pair (x, y) drawn from a population wit...
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Factor Investing: Hierarchical Ensemble Learning
We present a Bayesian hierarchical framework for both crosssectional an...
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Binscatter Regressions
We introduce the Stata (and R) package Binsreg, which implements the bin...
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Improved queuesize scaling for inputqueued switches via graph factorization
This paper studies the scaling of the expected total queue size in an n×...
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Information and Memory in Dynamic Resource Allocation
We propose a general framework, dubbed Stochastic Processing under Imper...
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Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
Probabilistic graphical models provide a flexible yet parsimonious frame...
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nprobust: Nonparametric KernelBased Estimation and Robust BiasCorrected Inference
Nonparametric kernel density and local polynomial regression estimators ...
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Racial Disparities in Voting Wait Times: Evidence from Smartphone Data
Equal access to voting is a core feature of democratic government. Using...
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Regularized deep learning with a nonconvex penalty
Regularization methods are often employed in deep learning neural networ...
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Regularized deep learning with nonconvex penalties
Regularization methods are often employed in deep learning neural networ...
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Constrained High Dimensional Statistical Inference
In typical high dimensional statistical inference problems, confidence i...
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Diagnostic Curves for Black Box Models
In safetycritical applications of machine learning, it is often necessa...
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Randomization Tests in Observational Studies with Staggered Adoption of Treatment
This paper studies inference in observational studies with timevarying ...
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Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks
In this paper, we utilize a connection between compositional kernels and...
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The University of Chicago Booth School of Business
The University of Chicago Booth School of Business is the graduate business school of the University of Chicago in Chicago, Illinois.