Rómer Rosales

ICML Conference 2011 Conference Paper

Active Learning from Crowds

• Yan Yan 0024
• Rómer Rosales
• Glenn Fung
• Jennifer G. Dy

JMLR Journal 2011 Journal Article

Bayesian Co-Training

• Shipeng Yu
• Balaji Krishnapuram
• Rómer Rosales
• R. Bharat Rao

Co-training (or more generally, co-regularization) has been a popular algorithm for semi-supervised learning in data with two feature representations (or views), but the fundamental assumptions underlying this type of models are still unclear. In this paper we propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view learning. This makes explicit the previously unstated assumptions of a large class of co-training type algorithms, and also clarifies the circumstances under which these assumptions fail. Building upon new insights from this model, we propose an improved method for co-training, which is a novel co-training kernel for Gaussian process classifiers. The resulting approach is convex and avoids local-maxima problems, and it can also automatically estimate how much each view should be trusted to accommodate noisy or unreliable views. The Bayesian co-training approach can also elegantly handle data samples with missing views, that is, some of the views are not available for some data points at learning time. This is further extended to an active sensing framework, in which the missing (sample, view) pairs are actively acquired to improve learning performance. The strength of active sensing model is that one actively sensed (sample, view) pair would improve the joint multi-view classification on all the samples. Experiments on toy data and several real world data sets illustrate the benefits of this approach. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

UAI Conference 2010 Conference Paper

Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario

• Yan Yan 0024
• Rómer Rosales
• Glenn Fung
• Jennifer G. Dy

Learning algorithms normally assume that there is at most one annotation or label per data point. However, in some scenarios, such as medical diagnosis and on-line collaboration, multiple annotations may be available. In either case, obtaining labels for data points can be expensive and time-consuming (in some circumstances groundtruth may not exist). Semi-supervised learning approaches have shown that utilizing the unlabeled data is often beneficial in these cases. This paper presents a probabilistic semi-supervised model and algorithm that allows for learning from both unlabeled and labeled data in the presence of multiple annotators. We assume that it is known what annotator labeled which data points. The proposed approach produces annotator models that allow us to provide (1) estimates of the true label and (2) annotator variable expertise for both labeled and unlabeled data. We provide numerical comparisons under various scenarios and with respect to standard semisupervised learning. Experiments showed that the presented approach provides clear advantages over multi-annotator methods that do not use the unlabeled data and over methods that do not use multi-labeler information.

NeurIPS Conference 2007 Conference Paper

Bayesian Co-Training

• Shipeng Yu
• Balaji Krishnapuram
• Harald Steck
• R. Rao
• Rómer Rosales

We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view learning. This makes explicit the previously unstated assumptions of a large class of co-training type algorithms, and also clarifies the circumstances under which these assumptions fail. Building upon new insights from this model, we propose an improved method for co-training, which is a novel co-training kernel for Gaussian process classifiers. The resulting approach is convex and avoids local-maxima problems, unlike some previous multi-view learning methods. Furthermore, it can automatically estimate how much each view should be trusted, and thus accommodate noisy or unreliable views. Experiments on toy data and real world data sets illustrate the benefits of this approach.

NeurIPS Conference 2005 Conference Paper

Learning Rankings via Convex Hull Separation

• Glenn Fung
• Rómer Rosales
• Balaji Krishnapuram

We propose efficient algorithms for learning ranking functions from or- der constraints between sets—i. e. classes—of training samples. Our al- gorithms may be used for maximizing the generalized Wilcoxon Mann Whitney statistic that accounts for the partial ordering of the classes: spe- cial cases include maximizing the area under the ROC curve for binary classification and its generalization for ordinal regression. Experiments on public benchmarks indicate that: (a) the proposed algorithm is at least as accurate as the current state-of-the-art; (b) computationally, it is sev- eral orders of magnitude faster and—unlike current methods—it is easily able to handle even large datasets with over 20, 000 samples.

ICML Conference 2004 Conference Paper

Learning to cluster using local neighborhood structure

• Rómer Rosales
• Kannan Achan
• Brendan J. Frey

UAI Conference 2003 Conference Paper

Learning Generative Models of Similarity Matrices

• Rómer Rosales
• Brendan J. Frey

We describe a probabilistic (generative) view of affinity matrices along with inference algorithms for a subclass of problems associated with data clustering. This probabilistic view is helpful in understanding different models and algorithms that are based on affinity functions OF the data. IN particular, we show how(greedy) inference FOR a specific probabilistic model IS equivalent TO the spectral clustering algorithm.It also provides a framework FOR developing new algorithms AND extended models. AS one CASE, we present new generative data clustering models that allow us TO infer the underlying distance measure suitable for the clustering problem at hand. These models seem to perform well in a larger class of problems for which other clustering algorithms (including spectral clustering) usually fail. Experimental evaluation was performed in a variety point data sets, showing excellent performance

NeurIPS Conference 2001 Conference Paper

Learning Body Pose via Specialized Maps

• Rómer Rosales
• Stan Sclaroff

A nonlinear supervised learning model, the Specialized Mappings Architecture (SMA), is described and applied to the estimation of human body pose from monocular images. The SMA consists of several specialized forward mapping functions and an inverse map(cid: 173) ping function. Each specialized function maps certain domains of the input space (image features) onto the output space (body pose parameters). The key algorithmic problems faced are those of learning the specialized domains and mapping functions in an op(cid: 173) timal way, as well as performing inference given inputs and knowl(cid: 173) edge of the inverse function. Solutions to these problems employ the EM algorithm and alternating choices of conditional indepen(cid: 173) dence assumptions. Performance of the approach is evaluated with synthetic and real video sequences of human motion.