We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential…

Game theory finds nowadays a broad range of applications in engineering and machine learning. However, in a derivative-free, expensive black-box cont…

Sparse alpha-norm regularization has many data-rich applications in Marketing and Economics. Alpha-norm, in contrast to lasso and ridge regularizatio…

We study in this paper the rate of convergence for learning distributions with the adversarial framework and Generative Adversarial Networks (GANs), …

We present a unified view of likelihood based Gaussian progress regression for simulation experiments exhibiting input-dependent noise. Replication p…

Global-local shrinkage approaches have proved vastly successful regularizing models of practical interest in machine learning applications. Most existing works have focused on the linear, Gaussian case. The current paper complements the existing lit…

Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of…

Randomized controlled trials play an important role in how Internet companies predict the impact of policy decisions and product changes. In these `d…

We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual …

The paper studies approximations and control of a processor sharing (PS) server where the service rate depends on the number of jobs occupying the se…

Regularization methods are often employed in deep learning neural networks (DNNs) to prevent overfitting. For penalty based methods for DNN regulariz…

We consider the problem of estimating the difference between two functional undirected graphical models with shared structures. In many applications,…

George C. Tiao was born in London in 1933. After graduating with a B.A. in Economics from National Taiwan University in 1955 he went to the US to obt…

In this paper we develop a methodology that we call split sampling methods to estimate high dimensional expectations and rare event probabilities. Sp…

We show that regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis. We develop a procedure that jointly re…

Scalable Data Augmentation (SDA) provides a framework for training deep learning models using auxiliary hidden layers. Scalable MCMC is available for…

We propose a model-based vulnerability index of the population from Uruguay to vector-borne diseases. We have available measurements of a set of vari…

We investigate an application in the automatic tuning of computer codes, an area of research that has come to prominence alongside the recent rise of…

Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to streaming real-time informati…

We develop a new class of dynamic multivariate Poisson count models that allow for fast online updating and we refer to these models as multivariate …

We propose the Bayesian bridge estimator for regularized regression and classification. Two key mixture representations for the Bayesian bridge model…

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