# Publications

## 2011

Crowdclustering: Using crowdsourcing to discover categories in collections of images (and other human-interpretable patterns.)

- R. Gomes, P. Welinder, A. Krause, and P. Perona (2011). Crowdclustering.
*Advances in Neural Information Processing Systems.*

Paper Bibtex Extended Technical Report Code

Ph.D. Thesis: Can we build automatic categorization systems that learn and add categories over time with minimal human supervision?

- R. Gomes (2011). Towards Open Ended Learning: Budgets, Model Selection, and Representation.

Thesis Bibtex

## 2010

Clustering via unsupervised learning of probabilistic discriminative classifiers (kernelized logistic regression).

- R. Gomes, A. Krause, and P. Perona (2010). Disciminative Clustering
by Regularized Information Maximization.
*Advances in Neural Information Processing Systems.*

Paper Bibtex Code

Near optimal selection of informative examples from data streams. These data examples may be used in nonparametric clustering or regression problems.

- R. Gomes and A. Krause (2010). Budgeted Nonparametric Learning
from Data Streams.
*Proceedings of the International Conference on Machine Learning.*

Paper Bibtex Long Version

## 2008

Incremental/online algorithm for mixture model clustering with the Dirichlet process mixture model. Automatically adjusts the number of clusters as evidence arrives. The method is based on variational approximate inference.

- R. Gomes, M. Welling, and P. Perona (2008). Incremental learning of
nonparametric Bayesian mixture models.
*Proceedings of Computer Vision and Pattern Recognition.*

Paper Extended Thesis Chapter Bibtex Code

Extends the techniques of the above paper to the topic model, an hierarchical extension of the mixture model suited to modeling documents and images.

- R. Gomes, M. Welling, and P. Perona (2008). Memory bounded inference
in topic models.
*Proceedings of the International Conference of Machine Learning.*

Paper Bibtex Video Lecture Slides

Note: Typos were corrected in this version of the paper. In a number of places the sufficient statistics \phi_{l}(x) were written instead as \phi_{kl}(x). (There is no dependence on k).