T. Jebara

发表

Bhattacharyya and Expected Likelihood Kernels

R. Kondor, T. Jebara, 2003 .

An SVM learning approach to robotic grasping

Peter K. Allen, Tony Jebara, Andrew T. Miller, 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

Group theoretical methods in machine learning

I. Kondor, T. Jebara, 2008 .

Bayesian face recognition

Alex Pentland, Tony Jebara, Baback Moghaddam, 2000, Pattern Recognit..

Maximum Entropy Discrimination

Tommi S. Jaakkola, Marina Meila, Tony Jebara, 1999, NIPS.

Probability Product Kernels

Tony Jebara, Risi Kondor, Andrew G. Howard, 2004, J. Mach. Learn. Res..

A multi-agent control framework for co-adaptation in brain-computer interfaces

Roy Fox, Liam Paninski, Tony Jebara, 2013, NIPS.

A Kernel Between Sets of Vectors

Tony Jebara, Risi Kondor, R. Kondor, 2003, ICML.

Feature Selection and Dualities in Maximum Entropy Discrimination pdf

Tommi S. Jaakkola, Tony Jebara, T. Jaakkola, 2000, UAI.

Multimodal person recognition using unconstrained audio and video

Tanzeem Choudhury, Tony Jebara, Brian Clarkson, 1998 .

$\propto$SVM for learning with label proportions pdf

Dong Liu, Shih-Fu Chang, Sanjiv Kumar, 2013, ICML 2013.

Discriminative, generative and imitative learning

Alex Pentland, Tony Jebara, A. Pentland, 2002 .

Nonstationary kernel combination

William Stafford Noble, Tony Jebara, Darrin P. Lewis, 2006, ICML.

Ellipsoidal Machines

Tony Jebara, Pannagadatta K. Shivaswamy, T. Jebara, 2007, AISTATS.

The Generalized CEM Algorithm

A. Pentland, T. Jebara, 1999, NIPS 1999.

SVM for learning with label proportions

Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang, 2013, ICML 2013.

On Learning with Label Proportions pdf

Shih-Fu Chang, Sanjiv Kumar, Tony Jebara, 2014, ArXiv.

Square Root Propagation

T. Jebara, Andrew G. Howard, 2005 .

Collaborative Place Models

Berk Kapicioglu, Robert E. Schapire, Tony Jebara, 2015, IJCAI.

CitySenseTM : multiscale space time clustering of GPS points and trajectories

T. Jebara, Markus Loecher, 2009 .

New Trends in Integrative Cognitive Science : Approaches to Development and Learning

T. Jebara, G. Deák, Marni Bartlett, 2006 .

Machine Learning Methods for ENSO Analysis and Prediction

Upmanu Lall, T. Jebara, A. Barnston, 2015 .

Deep Belief Nets for Topic Modeling Workshop on Knowledge-Powered Deep Learning for Text Mining (KPDLTM-2014)

E. P. Xing, T. Jebara, E. Xing, 2015 .

Structured Prediction of Generalized Matching Graphs

T. Jebara, Stuart Andrews, 2008 .

Structured Prediction of Degree Constrained Subgraphs

T. Jebara, Stuart Andrews, 2007 .

Learning with Degree-Based Subgraph Estimation

T. Jebara, Bert Huang, Tony Jebara, 2011 .

Model-Based Real-Time Face Tracking with Adaptive Texture Update

A. Pentland, T. Jebara, J. Ström, 2007 .

The LapSim: a learning environment for both experts and novices.

T. Jebara, J. McGinty, C. Ro, 2005, Studies in health technology and informatics.

Authors Certification of Participation Manuscript Title: B-matching for Identifying Authorship from Text Security Classification Certification Manuscript Title: B-matching for Identifying Authorship from Text Manuscript Title: B-matching for Identifying Authorship from Text B-matching for Identifyin

T. Jebara, 2022 .

Efficient MAP/ML similarity matching for visual recognition

Alex Pentland, Tony Jebara, Baback Moghaddam, 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

Multi Facet Learning in Hilbert Spaces

R. Kondor, T. Jebara, S. Ahnert, 2005 .

A novel drill set for the enhancement and assessment of robotic surgical performance.

Tony Jebara, Celina Imielinska, T. Jebara, 2005, Studies in health technology and informatics.

Identification and Targeting of Cortical Ensembles with Probabilistic 1 Graphical Models 2 3

T. Jebara, L. Carrillo-Reid, Shuting Han, 2017 .

Using Survival Models to Estimate User Engagement in Online Experiments

Brian St. Thomas, T. Jebara, Lucas Maystre, 2022, WWW.

SCMPS 2009 Organizing and Program Committees

Deborah Estrin, Benjamin N. Waber, Daniel Olguin Olguin, 2009 .

Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32

Eric P. Xing, Tony Jebara, E. Xing, 2014 .

Network Prediction with Degree Distributional Metric Learning

T. Jebara, Bert Huang, B. Shaw, 2009 .

Place Recommendation with Implicit Spatial Feedback

David S. Rosenberg, R. Schapire, T. Jebara, 2022 .

Robust learning algorithms for capturing oceanic dynamics and transport of Noctiluca blooms using linear dynamical models

T. Jebara, J. Goes, H. Gomes, 2019, PloS one.

Robust Algorithms for Capturing Population Dynamics and Transport in Oceanic Variables along Drifter Trajectories using Linear Dynamical Systems with Latent Variables

T. Jebara, J. Goes, H. Gomes, 2018, bioRxiv.

Orbit Learning using Convex Optimization

T. Jebara, 2004 .

Selectively Contextual Bandits

T. Jebara, Qifeng Qiao, A. Chandrashekar, 2022, ArXiv.

Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)

Tony Jebara, T. Jebara, 2003 .

Statistical Prediction of ENSO from Subsurface Sea Temperature Using a Nonlinear Dimensionality Reduction

Upmanu Lall, Tony Jebara, Carlos H.R. Lima, 2009, Journal of Climate.

Machine Learning

Tony Jebara, T. Jebara, 2003, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models

R. Yuste, T. Jebara, L. Carrillo-Reid, 2021, The Journal of Neuroscience.

Multistate analysis with infinite mixtures of Markov chains

T. Jebara, Lucas Maystre, R. Sanchis-Ojeda, 2022, UAI.

Maximum Weight b-Matching via Belief Propagation

Bert Huang, Tony Jebara, T. Jebara, 2006 .

Statistical imitative learning from perceptual data

Tony Jebara, A. Pentland, A. Pentland, 2002, Proceedings 2nd International Conference on Development and Learning. ICDL 2002.

Variational Autoencoders for Collaborative Filtering

Matthew D. Hoffman, Dawen Liang, Tony Jebara, 2018, WWW.

Computational Social Science

Lada A. Adamic, David Lazer, Albert-László Barabási, 2009, Science.

Multi-object tracking with representations of the symmetric group

Tony Jebara, Risi Kondor, Andrew G. Howard, 2007, AISTATS.

Large margin transformation learning

Tony Jebara, Andrew Howard, T. Jebara, 2009 .

Learning a Distance Metric from a Network

Bert Huang, Tony Jebara, Blake Shaw, 2011, NIPS.

Minimum Volume Embedding

Tony Jebara, Blake Shaw, T. Jebara, 2007, AISTATS.

Semi-supervised learning using greedy max-cut

Shih-Fu Chang, Jun Wang, Tony Jebara, 2013, J. Mach. Learn. Res..

Graph construction and b-matching for semi-supervised learning

Shih-Fu Chang, Jun Wang, Tony Jebara, 2009, ICML '09.

Semantic Concept Classification by Joint Semi-supervised Learning of Feature Subspaces and Support Vector Machines

Shih-Fu Chang, Alexander C. Loui, Tony Jebara, 2008, ECCV.

Graph transduction via alternating minimization

Shih-Fu Chang, Jun Wang, Tony Jebara, 2008, ICML '08.

3 D Structure from 2 D Motion

Alex Pentland, Ali Azarbayejani, Tony Jebara, 2001 .

3D structure from 2D motion

Alex Pentland, Ali J. Azarbayejani, Tony Jebara, 1999, IEEE Signal Process. Mag..

Collaborative Filtering via Rating Concentration

Bert Huang, Tony Jebara, T. Jebara, 2010, AISTATS.

Exact Graph Structure Estimation with Degree Priors

Bert Huang, Tony Jebara, T. Jebara, 2009, 2009 International Conference on Machine Learning and Applications.

Thompson Sampling for Noncompliant Bandits pdf

Tony Jebara, Andrew Stirn, T. Jebara, 2018, ArXiv.

Nicholas Ruozzi, Da Tang, Dawen Liang, 2019, ArXiv.

Correlated Variational Auto-Encoders

Nicholas Ruozzi, Dawen Liang, Tony Jebara, 2019, ICML.

Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour

Alex Pentland, Tony Jebara, A. Pentland, 1999, ICVS.

Frank-Wolfe Algorithms for Saddle Point Problems

Tony Jebara, Gauthier Gidel, Simon Lacoste-Julien, 2016, AISTATS.

Action-reaction learning : analysis and synthesis of human behaviour

T. Jebara, Tony Jebara, 1998 .

Making Pairwise Binary Graphical Models Attractive

Nicholas Ruozzi, Tony Jebara, N. Ruozzi, 2014, NIPS.

Approximating the Bethe Partition Function

Adrian Weller, Tony Jebara, T. Jebara, 2013, UAI.

Perfect Graphs and Graphical Modeling

Tony Jebara, T. Jebara, 2014, Tractability.

On MAP Inference by MWSS on Perfect Graphs pdf

Adrian Weller, Tony Jebara, T. Jebara, 2013, UAI.

Bethe Bounds and Approximating the Global Optimum

Adrian Weller, Tony Jebara, T. Jebara, 2012, AISTATS.

Identification and Targeting of Cortical Ensembles

Rafael Yuste, Tony Jebara, Luis Carrillo-Reid, 2017, bioRxiv.

Spectral Clustering and Embedding with Hidden Markov Models

Tony Jebara, Yingbo Song, Kapil Thadani, 2007, ECML.

Mixtures of eigenfeatures for real-time structure from texture

Alex Pentland, Tony Jebara, Kenneth B. Russell, 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

Fast Spectral Clustering via the Nyström Method

Anna Choromanska, Tony Jebara, Claire Monteleoni, 2013, ALT.

Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm

Alex Pentland, Tony Jebara, A. Pentland, 1998, NIPS.

Alternating Projection for Independent Component Analysis

T. Jebara, 2022 .

Multistate analysis with infinite mixtures of Markov chains

T. Jebara, Lucas Maystre, R. Sanchis-Ojeda, 2022, UAI.

New trends in Cognitive Science: Integrative approaches to learning and development

Marian Stewart Bartlett, Tony Jebara, Gedeon O. Deák, 2007, Neurocomputing.

Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure

William Stafford Noble, Tony Jebara, Darrin P. Lewis, 2006, Bioinform..

Artwork personalization at netflix

Justin Basilico, Ashok Chandrashekar, Tony Jebara, 2018, RecSys.

Structure preserving embedding

Tony Jebara, Blake Shaw, T. Jebara, 2009, ICML '09.

Item Recommendation with Variational Autoencoders and Heterogeneous Priors

Jie Yuan, Da Tang, Tony Jebara, 2018, DLRS@RecSys.

Accordion: A Trainable Simulator for Long-Term Interactive Systems

Justin Basilico, Tony Jebara, Ehtsham Elahi, 2021, RecSys.

Stochasticks: augmenting the billiards experience with probabilistic vision and wearable computers

Alex Pentland, Thad Starner, Tony Jebara, 1997, Digest of Papers. First International Symposium on Wearable Computers.

Large relative margin and applications

Tony Jebara, Pannagadatta K. Shivaswamy, T. Jebara, 2010 .

Bhattacharyya Expected Likelihood Kernels

Tony Jebara, Risi Kondor, R. Kondor, 2003, COLT.

Multitask Sparsity via Maximum Entropy Discrimination

Tony Jebara, T. Jebara, 2011, J. Mach. Learn. Res..

MAP Estimation, Message Passing, and Perfect Graphs pdf

Tony Jebara, T. Jebara, 2009, UAI.

Understanding the Bethe Approximation: When and How can it go Wrong?

Adrian Weller, Tony Jebara, David Sontag, 2014, UAI.

Bayesian Out-Trees pdf

Tony Jebara, T. Jebara, 2008, UAI.

Tree Dependent Identically Distributed Learning

Philip M. Long, Tony Jebara, T. Jebara, 2005 .

An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments

Daniel P. W. Ellis, Michael I. Mandel, Tony Jebara, 2006, NIPS.

Binary embeddings with structured hashed projections

Sanjiv Kumar, Yann LeCun, Mariusz Bojarski, 2015, ICML.

Multi-task feature and kernel selection for SVMs

Tony Jebara, T. Jebara, Tony Jebara, 2004, ICML.

A New Distribution on the Simplex with Auto-Encoding Applications

David A. Knowles, Tony Jebara, Andrew Stirn, 2019, NeurIPS.

Markov Models for Network-Behavior Modeling and Anonymization

Salvatore J. Stolfo, Tony Jebara, Yingbo Song, 2011 .

Gaussian and Wishart Hyperkernels

Tony Jebara, Risi Kondor, R. Kondor, 2006, NIPS.

Code Relatives: Detecting Similar Software Behavior

Gail E. Kaiser, Simha Sethumadhavan, Tony Jebara, 2015 .

Parametrized structure from motion for 3D adaptive feedback tracking of faces

Alex Pentland, Tony Jebara, A. Pentland, 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

On the Design of Estimators for Bandit Off-Policy Evaluation

Nikos Vlassis, Tony Jebara, Aurélien F. Bibaut, 2019, ICML.

Clustered blockwise PCA for representing visual data

Shree K. Nayar, Ko Nishino, Tony Jebara, 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Permutation invariant SVMs

Tony Jebara, Pannagadatta K. Shivaswamy, T. Jebara, 2006, ICML.

Code relatives: detecting similarly behaving software

Gail E. Kaiser, Simha Sethumadhavan, Jonathan Bell, 2016, SIGSOFT FSE.

Maximum Relative Margin and Data-Dependent Regularization

Tony Jebara, Pannagadatta K. Shivaswamy, T. Jebara, 2010, J. Mach. Learn. Res..

Collaborative Ranking for Local Preferences

Berk Kapicioglu, Robert E. Schapire, Tony Jebara, 2014, AISTATS.

Learning a Degree-Augmented Distance Metric from a Network

Bert Huang, Tony Jebara, T. Jebara, 2011 .

Subgoal Discovery for Hierarchical Dialogue Policy Learning pdf

Chong Wang, Jianfeng Gao, Xiujun Li, 2018, EMNLP.

When Enough is Enough: Location Tracking, Mosaic Theory, and Machine Learning

Steven M. Bellovin, Sebastian Zimmeck, Tony Jebara, 2014 .

Loopy Belief Propagation for Bipartite Maximum Weight b-Matching

Bert Huang, Tony Jebara, T. Jebara, 2007, AISTATS.

Generative Versus Discriminative Learning

Tony Jebara, T. Jebara, 2004 .

Adaptive Anonymity via b-Matching

Krzysztof Choromanski, Tony Jebara, Kui Tang, 2013, NIPS.

3D Pose Estimation and Normalization for Face Recognition

Tony Jebara, T. Jebara, Tony Jebara, 1995 .

Modularity and Specialized Learning: Reexamining Behavior-Based Artificial Intelligence

Jochen Triesch, Joanna J. Bryson, Tony Jebara, 2004 .

Initialization and Coordinate Optimization for Multi-way Matching

Da Tang, Tony Jebara, T. Jebara, 2016, AISTATS.

Structured Network Learning

Tony Jebara, T. Jebara, Stuart Andrews, 2006 .

Semistochastic Quadratic Bound Methods

Dimitri Kanevsky, Anna Choromanska, Aleksandr Y. Aravkin, 2013, ICLR 2013.

Stochastic Bound Majorization pdf

Anna Choromanska, Tony Jebara, T. Jebara, 2013, ArXiv.

Majorization for CRFs and Latent Likelihoods

Anna Choromanska, Tony Jebara, T. Jebara, 2012, NIPS.

Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA

Tony Jebara, T. Jebara, 2004, COLT.

Computational Social Science

Lada A. Adamic, A. Pentland, D. Lazer, 2009, Science.

ADMM SLIM: Sparse Recommendations for Many Users

Harald Steck, Tony Jebara, Maria Dimakopoulou, 2020, WSDM.

Images as bags of pixels

Tony Jebara, T. Jebara, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

Fast b-matching via Sufficient Selection Belief Propagation

Bert Huang, Tony Jebara, T. Jebara, 2011, AISTATS.