Home      Library      Discussions      About
Join us

@stat.berkeley.edu

Jul 13, 2016
Probability

There is another way to condition a Markov process to be in a fixed state at a large random time. It is described in the following theorem.

Markov processes conditioned on their location at large exponential …

Suppose that $(X_t)_{t \ge 0}$ is a one-dimensional Brownian motion with negative drift $-\mu$. It is possible to make sense of conditioning this pro…
Mar 21, 2018
Machine Learning
Computation

Error Estimation for Randomized Least-Squares Algorithms via the Bo…

Over the course of the past decade, a variety of randomized algorithms have been proposed for computing approximate least-squares (LS) solutions in l…
Mar 06, 2011
Methodology
Numerical Analysis

Pairwise ranking: choice of method can produce arbitrarily different …

We examine three methods for ranking by pairwise comparison: Principal Eigenvector, HodgeRank and Tropical Eigenvector. It is shown that the choice o…
Sep 11, 2017
Machine Learning
Distributed
Parallel
and Cluster Computing
Optimization and Control

GIANT: Globally Improved Approximate Newton Method for Distributed …

For distributed computing environments, we consider the canonical machine learning problem of empirical risk minimization (ERM) with quadratic regula…
Feb 05, 2012
Computer Science and Game Theory

Bundling Customers: How to Exploit Trust Among Customers to Maximize …

We consider an auction of identical digital goods to customers whose valuations are drawn independently from known distributions. Myerson's classic r…
Feb 04, 2012
Information Theory
Machine Learning

Lossy Compression via Sparse Linear Regression: Performance under M…

We study a new class of codes for lossy compression with the squared-error distortion criterion, designed using the statistical framework of high-dim…
Jan 15, 2014
Methodology

Kernel Density Estimation with Berkson Error

Given a sample $\{X_i\}_{i=1}^n$ from $f_X$, we construct kernel density estimators for $f_Y$, the convolution of $f_X$ with a known error density $f…
Jun 11, 2017
Methodology

Quantifying uncertainty in the attribution of human influence on seve…

Event attribution in the context of climate change seeks to understand the role of anthropogenic greenhouse gas emissions on extreme weather events, …
Oct 19, 2015
Machine Learning
Information Theory

Stochastically Transitive Models for Pairwise Comparisons: Statistica…

There are various parametric models for analyzing pairwise comparison data, including the Bradley-Terry-Luce (BTL) and Thurstone models, but their re…
Apr 14, 2011
Applications

Encoding and decoding V1 fMRI responses to natural images with sparse…

Functional MRI (fMRI) has become the most common method for investigating the human brain. However, fMRI data present some complications for statisti…
Jul 10, 2010
Machine Learning
Statistics Theory

Spectral clustering and the high-dimensional stochastic blockmodel

Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, represe…
Nov 13, 2014
Applications

Nonlinear predictive latent process models for integrating spatio-t…

Spatio-temporal prediction of levels of an environmental exposure is an important problem in environmental epidemiology. Our work is motivated by mul…
Aug 11, 2011
Conferences: NIPS
Statistics Theory
Methodology
Machine Learning

A More Powerful Two-Sample Test in High Dimensions using Random Pro…

We consider the hypothesis testing problem of detecting a shift between the means of two multivariate normal distributions in the high-dimensional se…
Jun 23, 2013
Methodology
Machine Learning

A Statistical Perspective on Algorithmic Leveraging

One popular method for dealing with large-scale data sets is sampling. For example, by using the empirical statistical leverage scores as an importan…
Jul 18, 2012
Machine Learning
Optimization and Control

Stochastic optimization and sparse statistical recovery: An optimal …

We develop and analyze stochastic optimization algorithms for problems in which the expected loss is strongly convex, and the optimum is (approximate…
Aug 23, 2017
Optimization and Control
Computational Complexity
Machine Learning

In this paper, we considered non-convex optimization settings and developed efficient variants of the trust region and adaptive cubic regularization methods in which the curvature information is approximated. For all of our proposed variants, under …

Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian…

We consider variants of trust-region and cubic regularization methods for non-convex optimization, in which the Hessian matrix is approximated. Under…
May 04, 2008
Information Theory

Network-based consensus averaging with general noisy channels

This paper focuses on the consensus averaging problem on graphs under general noisy channels. We study a particular class of distributed consensus al…
Nov 12, 2013
Machine Learning
Social and Information Networks
Statistics Theory
Physics and Society

Hypothesis Testing for Automated Community Detection in Networks

Community detection in networks is a key exploratory tool with applications in a diverse set of areas, ranging from finding communities in social and…
Jun 10, 2019
Machine Learning

Bayesian experimental design using regularized determinantal point …

In experimental design, we are given $n$ vectors in $d$ dimensions, and our goal is to select $k\ll n$ of them to perform expensive measurements, e.g…
Sep 04, 2008
Statistics Theory
Information Theory

Estimating divergence functionals and the likelihood ratio by convex …

We develop and analyze $M$-estimation methods for divergence functionals and the likelihood ratios of two probability distributions. Our method is ba…
There aren't more papers

Request Comment

or Invite via email
You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test
Test description