Ben Taskar

Q: What Schools Are Affiliated With Ben Taskar

Ben Taskar is affiliated with the following schools: University of California, Berkeley, Stanford University, University of Washington, University of Pennsylvania

Ben Taskar's AcademicInfluence.com Rankings

Ben Taskar

Computer Science

#1797

World Rank

#1863

Historical Rank

#858

USA Rank

Database

#7247

World Rank

#7498

Historical Rank

#868

USA Rank

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Computer Science

Ben Taskar's Degrees

PhD Computer Science Stanford University
Masters Computer Science Stanford University
Bachelors Computer Science Stanford University

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Why Is Ben Taskar Influential?

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According to Wikipedia, Ben Taskar was a professor and researcher in the area of machine learning and applications to computational linguistics and computer vision. He was a Magerman Term Associate Professor for Computer and Information Science at University of Pennsylvania. He co-directed PRiML: Penn Research in Machine Learning, a joint venture between the School of Engineering and Wharton. He was also a Distinguished Research Fellow at the Annenberg Center for Public Policy. At the University of Washington, he held the Boeing Professorship.

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Ben Taskar's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Max-Margin Markov Networks (2003) (1495)
Determinantal Point Processes for Machine Learning (2012) (888)
Discriminative Probabilistic Models for Relational Data (2002) (809)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (2007) (587)
Introduction to statistical relational learning (2007) (580)
Learning structured prediction models: a large margin approach (2005) (568)
Link Prediction in Relational Data (2003) (540)
Joint covariate selection and joint subspace selection for multiple classification problems (2010) (539)
Posterior Regularization for Structured Latent Variable Models (2010) (527)
Alignment by Agreement (2006) (509)
Discriminative learning of Markov random fields for segmentation of 3D scan data (2005) (427)
Learning from Partial Labels (2011) (379)
An Introduction to Conditional Random Fields for Relational Learning (2007) (378)
MODEC: Multimodal Decomposable Models for Human Pose Estimation (2013) (374)
An End-to-End Discriminative Approach to Machine Translation (2006) (321)
Multi-task feature selection (2006) (318)
Probabilistic Classification and Clustering in Relational Data (2001) (302)
Learning Probabilistic Models of Link Structure (2003) (302)
Selectivity estimation using probabilistic models (2001) (293)
Rich probabilistic models for gene expression (2001) (284)
Learning Probabilistic Models of Relational Structure (2001) (255)
Max-Margin Parsing (2004) (250)
k-DPPs: Fixed-Size Determinantal Point Processes (2011) (243)
Cascaded Models for Articulated Pose Estimation (2010) (233)
Markov Logic: A Unifying Framework for Statistical Relational Learning (2007) (213)
Learning associative Markov networks (2004) (205)
A Discriminative Matching Approach to Word Alignment (2005) (189)
Learning from ambiguously labeled images (2009) (184)
Expectation Maximization and Posterior Constraints (2007) (182)
Parsing human motion with stretchable models (2011) (168)
Probabilistic Relational Models (2014) (160)
Adaptive pose priors for pictorial structures (2010) (154)
Learning Determinantal Point Processes (2011) (147)
Movie/Script: Alignment and Parsing of Video and Text Transcription (2008) (147)
Global Inference for Entity and Relation Identification via Linear Programming Formulation (2007) (141)
Dependency Grammar Induction via Bitext Projection Constraints (2009) (141)
Predicting Structured Data (Neural Information Processing) (2007) (137)
Graphical Models in a Nutshell (2007) (137)
Structured Prediction, Dual Extragradient and Bregman Projections (2006) (127)
Structured Determinantal Point Processes (2010) (127)
Structured Prediction Cascades (2010) (126)
Near-Optimal MAP Inference for Determinantal Point Processes (2012) (120)
Understanding Objects in Detail with Fine-Grained Attributes (2014) (109)
Wiki-ly Supervised Part-of-Speech Tagging (2012) (105)
Probabilistic Models of Text and Link Structure for Hypertext Classification (2001) (99)
The Swarm at the Edge of the Cloud (2015) (99)
Word Alignment via Quadratic Assignment (2006) (97)
Learning the Parameters of Determinantal Point Process Kernels (2014) (93)
Shape-Based Object Detection via Boundary Structure Segmentation (2012) (88)
Bayesian Logic Programming: Theory and Tool (2007) (87)
Detecting and parsing architecture at city scale from range data (2010) (86)
Discovering Diverse and Salient Threads in Document Collections (2012) (84)
Object detection via boundary structure segmentation (2010) (82)
Exponentiated Gradient Algorithms for Large-margin Structured Classification (2004) (82)
Expectation-Maximization for Learning Determinantal Point Processes (2014) (79)
Better Alignments = Better Translations? (2008) (78)
Posterior vs Parameter Sparsity in Latent Variable Models (2009) (76)
Generative-Discriminative Basis Learning for Medical Imaging (2012) (72)
Generalization Bounds and Consistency for Structured Labeling (2007) (70)
Multi-View Learning over Structured and Non-Identical Outputs (2008) (70)
Probabilistic Entity-Relationship Models, PRMs, and Plate Models (2007) (69)
Structured Prediction via the Extragradient Method (2005) (65)
Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields (2008) (64)
The swarm at the edge of the cloud - A new perspective on wireless (2011) (61)
Talking pictures: Temporal grouping and dialog-supervised person recognition (2010) (59)
Sparsity in Dependency Grammar Induction (2010) (54)
Nystrom Approximation for Large-Scale Determinantal Processes (2013) (52)
Sidestepping Intractable Inference with Structured Ensemble Cascades (2010) (51)
Salient Montages from Unconstrained Videos (2014) (42)
Approximate Inference in Continuous Determinantal Processes (2013) (40)
Learning Probabilistic Relational Models with Structural Uncertainty (2000) (40)
Collective Stability in Structured Prediction: Generalization from One Example (2013) (40)
Posterior Sparsity in Unsupervised Dependency Parsing (2011) (40)
Relational Markov Networks (2007) (38)
BLOG: Probabilistic Models with Unknown Objects (2007) (36)
Learning on the Test Data: Leveraging Unseen Features (2003) (33)
Non-intrusive tongue machine interface (2014) (33)
Learning Tractable Word Alignment Models with Complex Constraints (2010) (33)
Learning Adaptive Value of Information for Structured Prediction (2013) (32)
A General and Unifying Framework for Feature Construction, in Image-Based Pattern Classification (2009) (30)
Label and Link Prediction in Relational Data (2003) (29)
Exploring repositories of scientific workflows (2010) (28)
Disease classification and prediction via semi-supervised dimensionality reduction (2011) (28)
Inductive Logic Programming in a Nutshell (2007) (27)
SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning (2013) (24)
Logic-based Formalisms for Statistical Relational Learning (2007) (22)
Approximate Inference in Continuous Determinantal Point Processes (2013) (22)
PAC-Bayesian Collective Stability (2014) (21)
Dynamic Structured Model Selection (2013) (21)
Efficient Second-Order Gradient Boosting for Conditional Random Fields (2015) (21)
A permutation-augmented sampler for DP mixture models (2007) (19)
Energy-Based Models (2007) (19)
Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction (2013) (18)
Learning Sparse Markov Network Structure via Ensemble-of-Trees Models (2009) (17)
Regularized Tensor Factorization for Multi-Modality Medical Image Classification (2011) (17)
The Tangent Earth Mover's Distance (2013) (16)
Controlling Complexity in Part-of-Speech Induction (2011) (15)
The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification (2013) (14)
Summarizing Unconstrained Videos Using Salient Montages (2017) (14)
An efficient algorithm for the symmetric principal minor assignment problem (2015) (14)
Feature Generation and Selection in Multi-Relational Statistical Learning (2007) (13)
Multi-Label Learning with Posterior Regularization (2014) (12)
Mixture-of-Parents Maximum Entropy Markov Models (2007) (10)
PostCAT - Posterior Constrained Alignment Toolkit (2009) (10)
A General Regression Framework for Learning String-to-String Mappings (2007) (9)
Efficient Algorithms for Max-Margin Structured Classification (2007) (7)
2 Graphical Models in a Nutshell (2008) (7)
Semi-Supervised Learning with Adversarially Missing Label Information (2010) (6)
Large margin methods for structured classification : Exponentiated gradient algorithms and PAC-Bayesian generalization bounds (2004) (6)
Stochastic Logic Programs: A Tutorial (2007) (5)
Statistical Relational Learning for Natural Language Information Extraction (2007) (4)
Application of trace-norm and low-rank matrix decomposition for computational anatomy (2010) (4)
Reinforcement Learning in Relational Domains: A Policy-Language Approach (2007) (4)
Towards a Detailed Understanding of Objects and Scenes in Natural Images (2012) (4)
Posterior Sparsity in Dependency Grammar Induction (2011) (3)
Lifted First-Order Probabilistic Inference (2007) (2)
Shape-based Object Detection via Boundary Structure (2011) (2)
The Design and Implementatin of IBAL: A General-Purpose Probabilistic Language (2007) (2)
Learning Tractable Word Alignment Models with Non-Markovian Constraints (2009) (1)
Expectation Maximization, Posterior Constraints, and Statistical Alignment (2007) (1)
Learning a New View of a Database: With an Application in Mammography (2007) (1)
6 Relational Markov Networks (2007) (1)
Learning as Search Optimization (2007) (1)
Stuctured Predictions Cascades (2010) (1)
Collective Stability in Structured Prediction: Appendix (2008) (0)
PAC-Bayesian Collective Stability Supplemental Material (2014) (0)
Structured Prediction Based on Discriminative Models (2007) (0)
Structured Prediction Using Probabilistic Models (2007) (0)
Semi-Supervised Learning with Adversarially Missing Label Information — Supplement (2010) (0)
Relational Dependency Networks (2007) (0)
Learning a kernel for discriminative , low-dimensional embedding of partially labeled data (2009) (0)
Kernel Conditional Graphical Models (2007) (0)
Proceedings of the {ICML} Workshop on Learning in Structured Output Spaces (2006) (0)
Recognizing Violence in Movies CIS 400 / 401 Project Final Report (2011) (0)
Modeling Structure via Graphical Models (2007) (0)
Empirical Analysis of Collective Stability (2013) (0)
Gaussian Process Belief Propagation (2007) (0)
Measuring Similarity with Kernels (2007) (0)
Enabling more accurate and efficient structured prediction (2013) (0)
A Politics Mash-Up (2008) (0)
LARGE MARGIN DISCRIMINATIVE LEARNING METHODS FOR ACOUSTIC MODELING (2006) (0)
Discriminative Learning of Prediction Suffix Trees with the Perceptron Algorithm (2007) (0)
Large-Scale Modeling of Diverse Paths using Structured k-DPPs (2012) (0)
Shape-Based Object Detection via Boundary Structure Segmentation (2012) (0)

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Other Resources About Ben Taskar

en.wikipedia.org

What Schools Are Affiliated With Ben Taskar?

Ben Taskar is affiliated with the following schools:

Ben Taskar's Academic­Influence.com Rankings