Ben Taskar

发表

Learning as Search Optimization

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Regularized Tensor Factorization for Multi-Modality Medical Image Classification

Ben Taskar, Christos Davatzikos, Nematollah Batmanghelich, 2011, MICCAI.

SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning

Ben Taskar, David J. Weiss, B. Taskar, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

PostCAT - Posterior Constrained Alignment Toolkit

Ben Taskar, João Graça, Kuzman Ganchev, 2009, Prague Bull. Math. Linguistics.

Probabilistic Models of Text and Link Structure for Hypertext Classification

Ben Taskar, Lise Getoor, Daphne Koller, 2001 .

k-DPPs: Fixed-Size Determinantal Point Processes

Ben Taskar, Alex Kulesza, B. Taskar, 2011, ICML.

Approximate Inference in Continuous Determinantal Processes

Ben Taskar, Emily B. Fox, Raja Hafiz Affandi, 2013, NIPS.

Learning Determinantal Point Processes

Ben Taskar, Alex Kulesza, B. Taskar, 2011, UAI.

Probabilistic Relational Models

Ben Taskar, Lise Getoor, L. Getoor, 2014, Encyclopedia of Social Network Analysis and Mining.

Lifted First-Order Probabilistic Inference

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Towards a Detailed Understanding of Objects and Scenes in Natural Images

Iasonas Kokkinos, Subhransu Maji, Ben Taskar, 2012 .

Modeling Structure via Graphical Models

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

A General Regression Framework for Learning String-to-String Mappings

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Dynamic Structured Model Selection

Ben Taskar, Benjamin Sapp, David J. Weiss, 2013, 2013 IEEE International Conference on Computer Vision.

Generative-Discriminative Basis Learning for Medical Imaging

Ben Taskar, Christos Davatzikos, Nematollah Batmanghelich, 2012, IEEE Transactions on Medical Imaging.

Approximate Inference in Continuous Determinantal Point Processes pdf

Ben Taskar, Emily B. Fox, Raja Hafiz Affandi, 2013, ArXiv.

Enabling more accurate and efficient structured prediction

Ben Taskar, David J. Weiss, 2013 .

Stuctured Predictions Cascades

Ben Taskar, David J. Weiss, B. Taskar, 2010 .

Learning from Partial Labels

Ben Taskar, Benjamin Sapp, Timothée Cour, 2011, J. Mach. Learn. Res..

The Tangent Earth Mover's Distance

Ben Taskar, Ofir Pele, B. Taskar, 2013, GSI.

PAC-Bayesian Collective Stability

Ben Taskar, Lise Getoor, Bert Huang, 2014, AISTATS.

The swarm at the edge of the cloud - A new perspective on wireless

Edward A. Lee, Alberto L. Sangiovanni-Vincentelli, David Blaauw, 2011, 2011 Symposium on VLSI Circuits - Digest of Technical Papers.

Discriminative Learning of Prediction Suffix Trees with the Perceptron Algorithm

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Word Alignment via Quadratic Assignment

Ben Taskar, Dan Klein, Michael I. Jordan, 2006, NAACL.

Non-intrusive tongue machine interface

Ben Taskar, Rajesh P. N. Rao, Qiao Zhang, 2014, CHI.

Sidestepping Intractable Inference with Structured Ensemble Cascades

Ben Taskar, Benjamin Sapp, David J. Weiss, 2010, NIPS.

Probabilistic Classification and Clustering in Relational Data

Ben Taskar, Daphne Koller, Eran Segal, 2001, IJCAI.

The Swarm at the Edge of the Cloud

Anthony Rowe, Edward A. Lee, David Blaauw, 2015, IEEE Design & Test.

Parsing human motion with stretchable models

Ben Taskar, Benjamin Sapp, David J. Weiss, 2011, CVPR 2011.

Efficient Algorithms for Max-Margin Structured Classification

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Expectation-Maximization for Learning Determinantal Point Processes

Ben Taskar, Alex Kulesza, Emily B. Fox, 2014, NIPS.

A General and Unifying Framework for Feature Construction, in Image-Based Pattern Classification

Ben Taskar, Christos Davatzikos, Nematollah Batmanghelich, 2009, IPMI.

Feature Generation and Selection in Multi-Relational Statistical Learning

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Structured Prediction Cascades

Ben Taskar, David J. Weiss, B. Taskar, 2010, AISTATS.

Statistical Relational Learning for Natural Language Information Extraction

Ben Taskar, Lise Getoor, 2007 .

Structured Prediction, Dual Extragradient and Bregman Projections

Ben Taskar, Michael I. Jordan, Simon Lacoste-Julien, 2006, J. Mach. Learn. Res..

Adaptive pose priors for pictorial structures

Ben Taskar, Benjamin Sapp, Christopher T. Jordan, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

A permutation-augmented sampler for DP mixture models

Ben Taskar, Michael I. Jordan, Percy Liang, 2007, ICML '07.

Understanding Objects in Detail with Fine-Grained Attributes

Iasonas Kokkinos, Subhransu Maji, Ben Taskar, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Posterior Sparsity in Dependency Grammar Induction

Ben Taskar, Kuzman Ganchev, Jennifer Gillenwater, 2011 .

Dependency Grammar Induction via Bitext Projection Constraints

Ben Taskar, Kuzman Ganchev, Jennifer Gillenwater, 2009, ACL/IJCNLP.

Learning Tractable Word Alignment Models with Complex Constraints

Ben Taskar, João Graça, Kuzman Ganchev, 2010, CL.

Detecting and parsing architecture at city scale from range data

Ben Taskar, Philippos Mordohai, Alexander Toshev, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

The Design and Implementatin of IBAL: A General-Purpose Probabilistic Language

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Learning Sparse Markov Network Structure via Ensemble-of-Trees Models

Ben Taskar, Shenghuo Zhu, Daniel D. Lee, 2009, AISTATS.

Talking pictures: Temporal grouping and dialog-supervised person recognition

Ben Taskar, Benjamin Sapp, Timothée Cour, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Mixture-of-Parents Maximum Entropy Markov Models pdf

Ben Taskar, Dan Klein, David S. Rosenberg, 2007, UAI.

Expectation Maximization and Posterior Constraints

Ben Taskar, João Graça, Kuzman Ganchev, 2007, NIPS.

Discovering Diverse and Salient Threads in Document Collections

Ben Taskar, Alex Kulesza, Jennifer Gillenwater, 2012, EMNLP.

Learning from ambiguously labeled images

Ben Taskar, Benjamin Sapp, Timothee Cour, 2009, CVPR.

Stochastic Logic Programs: A Tutorial

Ben Taskar, Lise Getoor, 2007 .

Structured Prediction Based on Discriminative Models

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Structured Prediction via the Extragradient Method

Ben Taskar, Michael I. Jordan, Simon Lacoste-Julien, 2005, NIPS.

Discriminative Probabilistic Models for Relational Data pdf

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

Shape-Based Object Detection via Boundary Structure Segmentation

Ben Taskar, Kostas Daniilidis, Alexander Toshev, 2012, International Journal of Computer Vision.

Energy-Based Models

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Rich probabilistic models for gene expression

Ben Taskar, Nir Friedman, Daphne Koller, 2001, ISMB.

Salient Montages from Unconstrained Videos

Ali Farhadi, Ben Taskar, Min Sun, 2014, ECCV.

Bayesian Logic Programming: Theory and Tool

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Reinforcement Learning in Relational Domains: A Policy-Language Approach

Ben Taskar, Lise Getoor, 2007 .

Learning associative Markov networks

Ben Taskar, Daphne Koller, Vassil Chatalbashev, 2004, ICML.

Sparsity in Dependency Grammar Induction

Ben Taskar, Fernando Pereira, João Graça, 2010, ACL.

Probabilistic Entity-Relationship Models, PRMs, and Plate Models

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Controlling Complexity in Part-of-Speech Induction

Ben Taskar, Fernando Pereira, João Graça, 2011, J. Artif. Intell. Res..

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Learning a New View of a Database: With an Application in Mammography

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Discriminative learning of Markov random fields for segmentation of 3D scan data

Ben Taskar, Dragomir Anguelov, Andrew Y. Ng, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Structured Determinantal Point Processes

Ben Taskar, Alex Kulesza, B. Taskar, 2010, NIPS.

Learning Adaptive Value of Information for Structured Prediction

Ben Taskar, David J. Weiss, B. Taskar, 2013, NIPS.

Collective Stability in Structured Prediction: Generalization from One Example

Ben Taskar, Lise Getoor, Bert Huang, 2013, ICML.

A Discriminative Matching Approach to Word Alignment

Ben Taskar, Dan Klein, Simon Lacoste-Julien, 2005, HLT.

Posterior Sparsity in Unsupervised Dependency Parsing

Ben Taskar, Fernando Pereira, João Graça, 2011, J. Mach. Learn. Res..

Object detection via boundary structure segmentation

Ben Taskar, Kostas Daniilidis, Alexander Toshev, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Wiki-ly Supervised Part-of-Speech Tagging

Ben Taskar, João Graça, Shen Li, 2012, EMNLP.

Max-Margin Markov Networks

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

Learning Probabilistic Models of Link Structure

Ben Taskar, Lise Getoor, Nir Friedman, 2003, J. Mach. Learn. Res..

Learning structured prediction models: a large margin approach

Ben Taskar, Carlos Guestrin, Daphne Koller, 2005, ICML.

Relational Markov Networks

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

MODEC: Multimodal Decomposable Models for Human Pose Estimation

Ben Taskar, Benjamin Sapp, B. Taskar, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Generalization Bounds and Consistency for Structured Labeling

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Empirical Analysis of Collective Stability

Ben Taskar, Bert Huang, Ben London, 2013 .

Near-Optimal MAP Inference for Determinantal Point Processes

Ben Taskar, Alex Kulesza, Jennifer Gillenwater, 2012, NIPS.

Semi-Supervised Learning with Adversarially Missing Label Information

Ben Taskar, Umar Syed, 2010, NIPS.

Inductive Logic Programming in a Nutshell

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Movie/Script: Alignment and Parsing of Video and Text Transcription

Ben Taskar, Christopher T. Jordan, Timothée Cour, 2008, ECCV.

Alignment by Agreement

Ben Taskar, Dan Klein, Percy Liang, 2006, NAACL.

An Introduction to Conditional Random Fields for Relational Learning

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields

Ben Taskar, Daniel D. Lee, Paul Vernaza, 2008, 2008 IEEE International Conference on Robotics and Automation.

BLOG: Probabilistic Models with Unknown Objects

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Shape-based Object Detection via Boundary Structure

Ben Taskar, Kostas Daniilidis, Alexander Toshev, 2011 .

Max-Margin Parsing

Ben Taskar, Dan Klein, Christopher D. Manning, 2004, EMNLP.

Joint covariate selection and joint subspace selection for multiple classification problems

Ben Taskar, Michael I. Jordan, Guillaume Obozinski, 2010, Stat. Comput..

The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification

Ben Taskar, Michael Werman, Amir Globerson, 2013, ICML.

Multi-View Learning over Structured and Non-Identical Outputs pdf

Ben Taskar, John Blitzer, João Graça, 2008, UAI.

Disease classification and prediction via semi-supervised dimensionality reduction

Ben Taskar, Christos Davatzikos, Kilian M. Pohl, 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

Predicting Structured Data (Neural Information Processing)

Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, 2007 .

Learning the Parameters of Determinantal Point Process Kernels

Ben Taskar, Ryan P. Adams, Emily B. Fox, 2014, ICML.

Nystrom Approximation for Large-Scale Determinantal Processes

Ben Taskar, Alex Kulesza, Emily B. Fox, 2013, AISTATS.

Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction

Ben Taskar, Luheng He, Jennifer Gillenwater, 2013, CoNLL.

Relational Dependency Networks

Ben Taskar, Lise Getoor, 2007 .

Markov Logic: A Unifying Framework for Statistical Relational Learning

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Exploring repositories of scientific workflows

Ben Taskar, Julia Stoyanovich, Susan Davidson, 2010, Wands '10.

Posterior Regularization for Structured Latent Variable Models

Ben Taskar, João Graça, Kuzman Ganchev, 2010, J. Mach. Learn. Res..

Large margin methods for structured classification : Exponentiated gradient algorithms and PAC-Bayesian generalization bounds

Ben Taskar, Peter L. Bartlett, Michael Collins, 2004 .

Posterior vs Parameter Sparsity in Latent Variable Models

Ben Taskar, João Graça, Kuzman Ganchev, 2009, NIPS.

Learning Probabilistic Models of Relational Structure

Ben Taskar, Lise Getoor, Nir Friedman, 2001, ICML.

Determinantal Point Processes for Machine Learning

Ben Taskar, Alex Kulesza, B. Taskar, 2012, Found. Trends Mach. Learn..

Exponentiated Gradient Algorithms for Large-margin Structured Classification

Ben Taskar, Peter L. Bartlett, Michael Collins, 2004, NIPS.

Efficient Second-Order Gradient Boosting for Conditional Random Fields

Ben Taskar, Tianqi Chen, Carlos Guestrin, 2015, AISTATS.

Link Prediction in Relational Data

Ben Taskar, Pieter Abbeel, Daphne Koller, 2003, NIPS.

Cascaded Models for Articulated Pose Estimation

Ben Taskar, Benjamin Sapp, Alexander Toshev, 2010, ECCV.

Fall 2010 Colloquium

Ben Taskar, 2010 .

Summarizing Unconstrained Videos Using Salient Montages

Ali Farhadi, Ben Taskar, Min Sun, 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Selectivity estimation using probabilistic models

Ben Taskar, Lise Getoor, Daphne Koller, 2001, SIGMOD '01.

Expectation Maximization, Posterior Constraints, and Statistical Alignment

Ben Taskar, B. Taskar, 2007, NIPS 2007.

Graphical Models in a Nutshell

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

Better Alignments = Better Translations?

Ben Taskar, João Graça, Kuzman Ganchev, 2008, ACL.

Application of trace-norm and low-rank matrix decomposition for computational anatomy

Ben Taskar, Christos Davatzikos, Ali Gooya, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

Introduction to statistical relational learning

Ben Taskar, Lise Getoor, L. Getoor, 2007 .

An End-to-End Discriminative Approach to Machine Translation

Ben Taskar, Dan Klein, Percy Liang, 2006, ACL.

Learning on the Test Data: Leveraging Unseen Features

Ben Taskar, Daphne Koller, Ming Fai Wong, 2003, ICML.

An efficient algorithm for the symmetric principal minor assignment problem

Ben Taskar, Alex Kulesza, Justin K. Rising, 2015 .