#### Filter Results:

- Full text PDF available (118)

#### Publication Year

2001

2016

- This year (0)
- Last 5 years (48)
- Last 10 years (94)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- 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 confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from… (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 hy-pertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, ignoring the correlations between them. Recently,… (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 the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a… (More)

We present a perceptron-style discriminative approach to machine translation in which large feature sets can be exploited. Unlike discriminative rerank-ing approaches, our system can take advantage of learned features in all stages of decoding. We first discuss several challenges to error-driven discrim-inative approaches. In particular, we explore… (More)

- Alex Kulesza, Ben Taskar
- Foundations and Trends in Machine Learning
- 2012

Number of pages: 225 pages Thank you very much for reading determinantal point processes for machine learning. As you may know, people have search numerous times for their chosen readings like this determinantal point processes for machine learning, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon,… (More)

- Kuzman Ganchev, João Graça, 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 posterior distributions of probabilistic models with latent variables. Posterior regularization separates model complexity from the complexity of structural constraints it… (More)

- Benjamin Sapp, Ben Taskar
- 2013 IEEE Conference on Computer Vision and…
- 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 to capture the wide range of appearance present in realistic, unconstrained images. In this paper, we instead propose a model of human pose that explicitly captures a… (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 goal is to learn parameters such that inference using the model reproduces correct answers on the training data. Our method relies on the expressive power of convex… (More)

We present a novel discriminative approach to parsing inspired by the large-margin criterion underlying support vector machines. Our formulation uses a factor-ization analogous to the standard dynamic programs for parsing. In particular, it allows one to efficiently learn a model which discriminates among the entire space of parse trees, as opposed to… (More)

- Dragomir Anguelov, Ben Taskar, +4 authors Andrew Y. Ng
- 2005 IEEE Computer Society Conference on Computer…
- 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-cut inference. The MRF models incorporate a large set of diverse features and enforce the preference that adjacent scan points have the same classification label. We… (More)