Ryan Gomes

NeurIPS Conference 2011 Conference Paper

Crowdclustering

• Ryan Gomes
• Peter Welinder
• Andreas Krause
• Pietro Perona

Is it possible to crowdsource categorization? Amongst the challenges: (a) each annotator has only a partial view of the data, (b) different annotators may have different clustering criteria and may produce different numbers of categories, (c) the underlying category structure may be hierarchical. We propose a Bayesian model of how annotators may approach clustering and show how one may infer clusters/categories, as well as annotator parameters, using this model. Our experiments, carried out on large collections of images, suggest that Bayesian crowdclustering works well and may be superior to single-expert annotations.

ICML Conference 2010 Conference Paper

Budgeted Nonparametric Learning from Data Streams

• Ryan Gomes
• Andreas Krause 0001

NeurIPS Conference 2010 Conference Paper

Discriminative Clustering by Regularized Information Maximization

• Andreas Krause
• Pietro Perona
• Ryan Gomes

Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled data set? We present a framework that simultaneously clusters the data and trains a discriminative classifier. We call it Regularized Information Maximization (RIM). RIM optimizes an intuitive information-theoretic objective function which balances class separation, class balance and classifier complexity. The approach can flexibly incorporate different likelihood functions, express prior assumptions about the relative size of different classes and incorporate partial labels for semi-supervised learning. In particular, we instantiate the framework to unsupervised, multi-class kernelized logistic regression. Our empirical evaluation indicates that RIM outperforms existing methods on several real data sets, and demonstrates that RIM is an effective model selection method.

ICML Conference 2008 Conference Paper

Memory bounded inference in topic models

• Ryan Gomes
• Max Welling
• Pietro Perona