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- Ben Taskar, Carlos Guestrin, Daphne Koller
- NIPS
- 2003

In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidenceâ€¦ (More)

- Ben Taskar, Pieter Abbeel, Daphne Koller
- UAI
- 2002

In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hyÂ pertext classification, the labels ofâ€¦ (More)

- Percy Liang, Ben Taskar, Dan Klein
- HLT-NAACL
- 2006

We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between theâ€¦ (More)

- Benjamin Sapp, Ben Taskar
- 2013 IEEE Conference on Computer Vision andâ€¦
- 2013

We propose a multimodal, decomposable model for articulated human pose estimation in monocular images. A typical approach to this problem is to use a linear structured model, which struggles toâ€¦ (More)

We present a perceptron-style discriminative approach to machine translation in which large feature sets can be exploited. Unlike discriminative reranking approaches, our system can take advantage ofâ€¦ (More)

- Kuzman Ganchev, JoÃ£o GraÃ§a, Jennifer Gillenwater, Ben Taskar
- Journal of Machine Learning Research
- 2010

We present Posterior Regularization, a probabilistic framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posteriorâ€¦ (More)

We consider large margin estimation in a broad range of prediction models where inference involves solving combinatorial optimization problems, for example, weighted graph-cuts or matchings. Our goalâ€¦ (More)

- TimothÃ©e Cour, Benjamin Sapp, Ben Taskar
- Computer Vision, A Reference Guide
- 2011

We address the problem of partially-labeled multiclass cla sification, where instead of a single label per instance, the algorithm is given a candidate set of la bels, only one of which is correct.â€¦ (More)

- Dragomir Anguelov, Ben Taskar, +4 authors Andrew Y. Ng
- 2005 IEEE Computer Society Conference on Computerâ€¦
- 2005

We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov random fields (MRFs) which support efficient graph-cutâ€¦ (More)

- JoÃ£o GraÃ§a, Kuzman Ganchev, Ben Taskar
- NIPS
- 2007

The expectation maximization (EM) algorithm is a widely used maximum likelihood estimation procedure for statistical models when the values of some of the variables in the model are not observed.â€¦ (More)