Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition. - Paul Gustafson; Peter Carbonetto; Natalie Thompson; Nando de Freitas (AISTATS, 2003)

A Statistical Model for General Contextual Object Recognition. - Peter Carbonetto; Nando de Freitas; Kobus Barnard (ECCV (1), 2004)

A Constrained Semi-supervised Learning Approach to Data Association. - Hendrik Kück; Peter Carbonetto; Nando de Freitas (ECCV (3), 2004)

Nonparametric Bayesian Logic. - Peter Carbonetto; Jacek Kisynski; Nando de Freitas; David Poole 001 (UAI, 2005)

A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues. - Peter Carbonetto; Gyuri Dorkó; Cordelia Schmid; Hendrik Kück; Nando de Freitas (Toward Category-Level Object Recognition, 2006)

Conditional mean field. - Peter Carbonetto; Nando de Freitas (NIPS, 2006)

An interior-point stochastic approximation method and an L1-regularized delta rule. - Peter Carbonetto; Mark Schmidt 001; Nando de Freitas (NIPS, 2008)

Learning to Recognize Objects with Little Supervision. - Peter Carbonetto; Gyuri Dorkó; Cordelia Schmid; Hendrik Kück; Nando de Freitas (Int. J. Comput. Vis., 2008)

A Stochastic approximation method for inference in probabilistic graphical models. - Peter Carbonetto; Matthew King; Firas Hamze (NIPS, 2009)

Creating and sharing reproducible research code the workflowr way. - John D. Blischak; Peter Carbonetto; Matthew Stephens (F1000Research, 2019)

Non-negative matrix factorization algorithms greatly improve topic model fits. - Peter Carbonetto; Abhishek Sarkar; Zihao Wang; Matthew Stephens (CoRR, 2021)

Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage. - Zhengrong Xing; Peter Carbonetto; Matthew Stephens (J. Mach. Learn. Res., 2021)

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression. - Youngseok Kim; Wei Wang; Peter Carbonetto; Matthew Stephens (J. Mach. Learn. Res., 2024)