Peter Carbonetto

发表

Creating and sharing reproducible research code the workflowr way

Matthew Stephens, John D Blischak, Peter Carbonetto, 2019, F1000Research.

An interior-point stochastic approximation method and an L1-regularized delta rule

Mark W. Schmidt, Nando de Freitas, Peter Carbonetto, 2008, NIPS.

A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming

Mihai Anitescu, Matthew Stephens, Peter Carbonetto, 2018, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

A Stochastic approximation method for inference in probabilistic graphical models

Matthew King, Firas Hamze, Peter Carbonetto, 2009, NIPS.

Conditional mean field

Nando de Freitas, Peter Carbonetto, N. D. Freitas, 2006, NIPS.

Learning a contingently acyclic, probabilistic relational model of a social network

Peter Carbonetto, David Poole, Michael Chiang, 2009 .

Learning to Recognize Objects with Little Supervision

Cordelia Schmid, Nando de Freitas, Gyuri Dorkó, 2008, International Journal of Computer Vision.

Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition

Nando de Freitas, Peter Carbonetto, Paul Gustafson, 2003, AISTATS.

New probabilistic inference algorithms that harness the strengths of variational and Monte Carlo methods

Peter Carbonetto, P. Carbonetto, 2009 .

A simple new approach to variable selection in regression, with application to genetic fine-mapping

Matthew Stephens, Peter Carbonetto, Gao T. Wang, 2018, bioRxiv.

A Framework for Reproducible and Collaborative Data Science [R package workflowr version 1.6.2]

John D. Blischak, Matthew Stephens, Peter Carbonetto, 2020 .

Nonparametric Bayesian Logic

Nando de Freitas, David Poole, Peter Carbonetto, 2005, UAI.

A Statistical Model for General Contextual Object Recognition

Nando de Freitas, Kobus Barnard, Peter Carbonetto, 2004, ECCV.

Why can’t José read? The problem of learning semantic associations in a robot environment

Peter Carbonetto, Nando De Freitas, P. Carbonetto, 2003, HLT-NAACL 2003.

A Constrained Semi-supervised Learning Approach to Data Association

Nando de Freitas, Peter Carbonetto, Hendrik Kück, 2004, ECCV.

Flexible signal denoising via flexible empirical Bayes shrinkage

Matthew Stephens, Peter Carbonetto, Zhengrong Xing, 2016 .

Polygenic Modeling with Bayesian Sparse Linear Mixed Models

Xiang Zhou, Matthew Stephens, Peter Carbonetto, 2012, PLoS genetics.

Unsupervised Statistical Models for General Object Recognition

Peter Carbonetto, 2003 .

A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

Cordelia Schmid, Nando de Freitas, Gyuri Dorkó, 2006, Toward Category-Level Object Recognition.

stephenslab/gtexresults: FLASH/MASH pipeline updated 2021

Peter Carbonetto, Sarah Urbut, gaow, 2021 .

Non-negative matrix factorization algorithms greatly improve topic model fits pdf

Matthew Stephens, Zihao Wang, Peter Carbonetto, 2021, ArXiv.

Genetic Background Limits Generalizability of Genotype-Phenotype Relationships

Abraham A. Palmer, Peter Carbonetto, P. Carbonetto, 2016, Neuron.

Genome-wide association study of behavioral, physiological and gene expression traits in commercially available outbred CFW mice

Peter Carbonetto, Shyam Gopalakrishnan, J. Pritchard, 2016, Nature Genetics.