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ORCIDTony Jebara
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- affiliation: Spotify, New York, NY, USA
- affiliation (former): Columbia University, New York City, USA
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2020 – today
- 2023
[c85]Himan Abdollahpouri
, Zahra Nazari, Alex Gain, Clay Gibson, Maria Dimakopoulou, Jesse Anderton, Benjamin A. Carterette, Mounia Lalmas, Tony Jebara:
Calibrated Recommendations as a Minimum-Cost Flow Problem. WSDM 2023: 571-579- 2022
- [c84]Lucas Maystre, Tiffany Wu, Roberto Sanchis-Ojeda, Tony Jebara:
Multistate analysis with infinite mixtures of Markov chains. UAI 2022: 1350-1359 - [c83]Praveen Chandar, Brian St. Thomas, Lucas Maystre, Vijay Pappu, Roberto Sanchis-Ojeda, Tiffany Wu, Ben Carterette, Mounia Lalmas, Tony Jebara:
Using Survival Models to Estimate User Engagement in Online Experiments. WWW 2022: 3186-3195 - [i27]Claudia V. Roberts, Maria Dimakopoulou, Qifeng Qiao, Ashok Chandrashekar, Tony Jebara:
Selectively Contextual Bandits. CoRR abs/2205.04528 (2022) - 2021
- [c82]James McInerney, Ehtsham Elahi, Justin Basilico, Yves Raimond, Tony Jebara:
Accordion: A Trainable Simulator forLong-Term Interactive Systems. RecSys 2021: 102-113 - [c81]David Hubbard, Benoit Rostykus, Yves Raimond, Tony Jebara:
Beta Survival Models. SPACA 2021: 22-39 - [i26]Jingxi Xu, Da Tang, Tony Jebara:
Active Multitask Learning with Committees. CoRR abs/2103.13420 (2021) - 2020
- [c80]Harald Steck, Maria Dimakopoulou, Nickolai Riabov, Tony Jebara:
ADMM SLIM: Sparse Recommendations for Many Users. WSDM 2020: 555-563
2010 – 2019
- 2019
- [c79]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. DGS@ICLR 2019 - [c78]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. ICML 2019: 6135-6144 - [c77]Nikos Vlassis, Aurélien Bibaut, Maria Dimakopoulou, Tony Jebara:
On the Design of Estimators for Bandit Off-Policy Evaluation. ICML 2019: 6468-6476 - [c76]Maria Dimakopoulou, Nikos Vlassis, Tony Jebara:
Marginal Posterior Sampling for Slate Bandits. IJCAI 2019: 2223-2229 - [c75]Andrew Stirn, Tony Jebara, David A. Knowles:
A New Distribution on the Simplex with Auto-Encoding Applications. NeurIPS 2019: 13670-13680 - [c74]Ehtsham Elahi, Wei Wang, Dave Ray, Aish Fenton, Tony Jebara:
Variational low rank multinomials for collaborative filtering with side-information. RecSys 2019: 340-347 - [i25]David Hubbard, Benoit Rostykus, Yves Raimond, Tony Jebara:
Beta Survival Models. CoRR abs/1905.03818 (2019) - [i24]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. CoRR abs/1905.05335 (2019) - [i23]Andrew Stirn, Tony Jebara, David A. Knowles:
A New Distribution on the Simplex with Auto-Encoding Applications. CoRR abs/1905.12052 (2019) - [i22]Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara:
Learning Correlated Latent Representations with Adaptive Priors. CoRR abs/1906.06419 (2019) - 2018
- [c73]Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara:
Subgoal Discovery for Hierarchical Dialogue Policy Learning. EMNLP 2018: 2298-2309 - [c72]Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan, Jie Yuan, Da Tang, Tony Jebara:
Item Recommendation with Variational Autoencoders and Heterogeneous Priors. DLRS@RecSys 2018: 10-14 - [c71]Fernando Amat Gil, Ashok Chandrashekar, Tony Jebara, Justin Basilico:
Artwork personalization at netflix. RecSys 2018: 487-488 - [c70]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. WWW 2018: 689-698 - [i21]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. CoRR abs/1802.05814 (2018) - [i20]Tony Jebara:
A refinement of Bennett's inequality with applications to portfolio optimization. CoRR abs/1804.05454 (2018) - [i19]Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara:
Subgoal Discovery for Hierarchical Dialogue Policy Learning. CoRR abs/1804.07855 (2018) - [i18]Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan, Jie Yuan, Da Tang, Tony Jebara:
Item Recommendation with Variational Autoencoders and Heterogenous Priors. CoRR abs/1807.06651 (2018) - [i17]Andrew Stirn, Tony Jebara:
Thompson Sampling for Noncompliant Bandits. CoRR abs/1812.00856 (2018) - 2017
- [c69]Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien:
Frank-Wolfe Algorithms for Saddle Point Problems. AISTATS 2017: 362-371 - [c68]Da Tang, Tony Jebara:
Initialization and Coordinate Optimization for Multi-way Matching. AISTATS 2017: 1385-1393 - [c67]Sebastian Zimmeck, Jie S. Li, Hyungtae Kim, Steven M. Bellovin, Tony Jebara:
A Privacy Analysis of Cross-device Tracking. USENIX Security Symposium 2017: 1391-1408 - 2016
- [c66]Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara:
Bethe Learning of Graphical Models via MAP Decoding. AISTATS 2016: 1096-1104 - [c65]Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun:
Binary embeddings with structured hashed projections. ICML 2016: 344-353 - [c64]Fang-Hsiang Su, Jonathan Bell, Kenneth Harvey, Simha Sethumadhavan, Gail E. Kaiser, Tony Jebara:
Code relatives: detecting similarly behaving software. SIGSOFT FSE 2016: 702-714 - [i16]Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien:
Frank-Wolfe Algorithms for Saddle Point Problems. CoRR abs/1610.07797 (2016) - [i15]Da Tang, Tony Jebara:
Initialization and Coordinate Optimization for Multi-way Matching. CoRR abs/1611.00838 (2016) - 2015
- [c63]Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara:
Collaborative Place Models. IJCAI 2015: 3612-3618 - [i14]Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara:
Bethe Learning of Conditional Random Fields via MAP Decoding. CoRR abs/1503.01228 (2015) - [i13]Krzysztof Choromanski, Tony Jebara:
Coloring tournaments with forbidden substructures. CoRR abs/1504.01119 (2015) - [i12]Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun:
Binary embeddings with structured hashed projections. CoRR abs/1511.05212 (2015) - 2014
- [c62]Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara:
Collaborative Ranking for Local Preferences. AISTATS 2014: 466-474 - [c61]Adrian Weller, Tony Jebara:
Clamping Variables and Approximate Inference. NIPS 2014: 909-917 - [c60]Nicholas Ruozzi, Tony Jebara:
Making Pairwise Binary Graphical Models Attractive. NIPS 2014: 1772-1780 - [c59]Adrian Weller, Tony Jebara:
Approximating the Bethe Partition Function. UAI 2014: 858-867 - [c58]Adrian Weller, Kui Tang, Tony Jebara, David A. Sontag:
Understanding the Bethe Approximation: When and How can it go Wrong? UAI 2014: 868-877 - [c57]Aleksandr Y. Aravkin, Anna Choromanska, Dimitri Kanevsky, Tony Jebara:
Semistochastic Quadratic Bound Methods for Convex and Nonconvex Learning Problems. ICLR (Workshop Poster) 2014 - [p1]Tony Jebara:
Perfect Graphs and Graphical Modeling. Tractability 2014: 39-68 - [i11]Adrian Weller, Tony Jebara:
Approximating the Bethe partition function. CoRR abs/1401.0044 (2014) - [i10]Felix X. Yu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang:
On Learning with Label Proportions. CoRR abs/1402.5902 (2014) - 2013
- [j9]Jun Wang, Tony Jebara, Shih-Fu Chang:
Semi-supervised learning using greedy max-cut. J. Mach. Learn. Res. 14(1): 771-800 (2013) - [c56]Adrian Weller, Tony Jebara:
Bethe Bounds and Approximating the Global Optimum. AISTATS 2013: 618-631 - [c55]Anna Choromanska, Tony Jebara, Hyungtae Kim, Mahesh Mohan, Claire Monteleoni:
Fast Spectral Clustering via the Nyström Method. ALT 2013: 367-381 - [c54]Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang:
\(\propto\)SVM for Learning with Label Proportions. ICML (3) 2013: 504-512 - [c53]Josh Merel, Roy Fox, Tony Jebara, Liam Paninski:
A multi-agent control framework for co-adaptation in brain-computer interfaces. NIPS 2013: 2841-2849 - [c52]Krzysztof Choromanski, Tony Jebara, Kui Tang:
Adaptive Anonymity via b-Matching. NIPS 2013: 3192-3200 - [c51]Adrian Weller, Tony Jebara:
On MAP Inference by MWSS on Perfect Graphs. UAI 2013 - [i9]Adrian Weller, Tony Jebara:
Bethe Bounds and Approximating the Global Optimum. CoRR abs/1301.0015 (2013) - [i8]Tony Jebara, Tommi S. Jaakkola:
Feature Selection and Dualities in Maximum Entropy Discrimination. CoRR abs/1301.3865 (2013) - [i7]Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang:
$\propto$SVM for learning with label proportions. CoRR abs/1306.0886 (2013) - [i6]Anna Choromanska, Tony Jebara:
Stochastic Bound Majorization. CoRR abs/1309.5605 (2013) - [i5]Adrian Weller, Tony Jebara:
On MAP Inference by MWSS on Perfect Graphs. CoRR abs/1309.6872 (2013) - 2012
- [c50]Tony Jebara, Anna Choromanska:
Majorization for CRFs and Latent Likelihoods. NIPS 2012: 566-574 - [i4]Tony Jebara:
MAP Estimation, Message Passing, and Perfect Graphs. CoRR abs/1205.2639 (2012) - [i3]Tony Jebara:
Bayesian Out-Trees. CoRR abs/1206.3269 (2012) - [i2]Andrew G. Howard, Tony Jebara:
Dynamical Systems Trees. CoRR abs/1207.4148 (2012) - 2011
- [j8]Tony Jebara:
Multitask Sparsity via Maximum Entropy Discrimination. J. Mach. Learn. Res. 12: 75-110 (2011) - [c49]Arezu Moghadam, Tony Jebara, Henning Schulzrinne:
A markov routing algorithm for mobile DTNs based on spatio-temporal modeling of human movement data. MSWiM 2011: 323-332 - [c48]Blake Shaw, Bert Huang, Tony Jebara:
Learning a Distance Metric from a Network. NIPS 2011: 1899-1907 - [c47]Pannagadatta K. Shivaswamy, Tony Jebara:
Variance Penalizing AdaBoost. NIPS 2011: 1908-1916 - [c46]Bert Huang, Tony Jebara:
Fast b-matching via Sufficient Selection Belief Propagation. AISTATS 2011: 361-369 - 2010
- [j7]Pannagadatta K. Shivaswamy, Tony Jebara:
Maximum Relative Margin and Data-Dependent Regularization. J. Mach. Learn. Res. 11: 747-788 (2010) - [c45]Pannagadatta K. Shivaswamy, Tony Jebara:
Laplacian Spectrum Learning. ECML/PKDD (3) 2010: 261-276 - [c44]Bert Huang, Tony Jebara:
Collaborative Filtering via Rating Concentration. AISTATS 2010: 334-341 - [c43]Pannagadatta K. Shivaswamy, Tony Jebara:
Empirical Bernstein Boosting. AISTATS 2010: 733-740
2000 – 2009
- 2009
- [c42]Tony Jebara, Jun Wang, Shih-Fu Chang:
Graph construction and b-matching for semi-supervised learning. ICML 2009: 441-448 - [c41]Blake Shaw, Tony Jebara:
Structure preserving embedding. ICML 2009: 937-944 - [c40]Bert Huang, Tony Jebara:
Exact Graph Structure Estimation with Degree Priors. ICMLA 2009: 111-118 - [c39]Pannagadatta K. Shivaswamy, Tony Jebara:
Structured Prediction with Relative Margin. ICMLA 2009: 281-287 - [c38]Andrew G. Howard, Tony Jebara:
Transformation Learning Via Kernel Alignment. ICMLA 2009: 301-308 - [c37]Adrian Weller, Daniel P. W. Ellis, Tony Jebara:
Structured Prediction Models for Chord Transcription of Music Audio. ICMLA 2009: 590-595 - [c36]Tony Jebara:
MAP Estimation, Message Passing, and Perfect Graphs. UAI 2009: 258-267 - [i1]Bert Huang, Tony Jebara:
Approximating the Permanent with Belief Propagation. CoRR abs/0908.1769 (2009) - 2008
- [c35]Wei Jiang, Shih-Fu Chang, Tony Jebara, Alexander C. Loui:
Semantic Concept Classification by Joint Semi-supervised Learning of Feature Subspaces and Support Vector Machines. ECCV (4) 2008: 270-283 - [c34]Jun Wang, Tony Jebara, Shih-Fu Chang:
Graph transduction via alternating minimization. ICML 2008: 1144-1151 - [c33]Pannagadatta K. Shivaswamy, Tony Jebara:
Relative Margin Machines. NIPS 2008: 1481-1488 - [c32]Tony Jebara:
Bayesian Out-Trees. UAI 2008: 315-324 - 2007
- [j6]Gedeon O. Deák, Marni Stewart Bartlett, Tony Jebara:
New trends in Cognitive Science: Integrative approaches to learning and development. Neurocomputing 70(13-15): 2139-2147 (2007) - [c31]Tony Jebara, Yingbo Song, Kapil Thadani:
Spectral Clustering and Embedding with Hidden Markov Models. ECML 2007: 164-175 - [c30]Andrew G. Howard, Tony Jebara:
Learning Monotonic Transformations for Classification. NIPS 2007: 681-688 - [c29]Tony Jebara, Yingbo Song, Kapil Thadani:
Density Estimation under Independent Similarly Distributed Sampling Assumptions. NIPS 2007: 713-720 - [c28]Bert Huang, Tony Jebara:
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching. AISTATS 2007: 195-202 - [c27]Risi Kondor, Andrew G. Howard, Tony Jebara:
Multi-object tracking with representations of the symmetric group. AISTATS 2007: 211-218 - [c26]Blake Shaw, Tony Jebara:
Minimum Volume Embedding. AISTATS 2007: 460-467 - [c25]Pannagadatta K. Shivaswamy, Tony Jebara:
Ellipsoidal Machines. AISTATS 2007: 484-491 - 2006
- [j5]Darrin P. Lewis, Tony Jebara, William Stafford Noble:
Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure. Bioinform. 22(22): 2753-2760 (2006) - [c24]Tony Jebara, Vlad Shchogolev:
B-Matching for Spectral Clustering. ECML 2006: 679-686 - [c23]Darrin P. Lewis, Tony Jebara, William Stafford Noble:
Nonstationary kernel combination. ICML 2006: 553-560 - [c22]Pannagadatta K. Shivaswamy, Tony Jebara:
Permutation invariant SVMs. ICML 2006: 817-824 - [c21]Risi Kondor, Tony Jebara:
Gaussian and Wishart Hyperkernels. NIPS 2006: 729-736 - [c20]Michael I. Mandel, Daniel P. W. Ellis, Tony Jebara:
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments. NIPS 2006: 953-960 - 2005
- [j4]Ko Nishino, Shree K. Nayar, Tony Jebara:
Clustered Blockwise PCA for Representing Visual Data. IEEE Trans. Pattern Anal. Mach. Intell. 27(10): 1675-1679 (2005) - 2004
- [j3]Tony Jebara, Risi Kondor, Andrew G. Howard:
Probability Product Kernels. J. Mach. Learn. Res. 5: 819-844 (2004) - [c19]Tony Jebara:
Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA. COLT 2004: 609-623 - [c18]Tony Jebara:
Multi-task feature and kernel selection for SVMs. ICML 2004 - [c17]Raphael Pelossof, Andrew T. Miller, Peter K. Allen, Tony Jebara:
An SVM Learning Approach to Robotic Grasping. ICRA 2004: 3512-3518 - [c16]Andrew G. Howard, Tony Jebara:
Dynamical Systems Trees. UAI 2004: 260-267 - 2003
- [c15]Tony Jebara:
Convex Invariance Learning. AISTATS 2003: 149-156 - [c14]Tony Jebara, Risi Kondor:
Bhattacharyya Expected Likelihood Kernels. COLT 2003: 57-71 - [c13]Tony Jebara:
Images as Bags of Pixels. ICCV 2003: 265-272 - [c12]Risi Kondor, Tony Jebara:
A Kernel Between Sets of Vectors. ICML 2003: 361-368 - 2000
- [j2]Baback Moghaddam, Tony Jebara, Alex Pentland:
Bayesian face recognition. Pattern Recognit. 33(11): 1771-1782 (2000) - [c11]Tony Jebara, Alex Pentland:
On Reversing Jensen's Inequality. NIPS 2000: 231-237 - [c10]Tony Jebara, Tommi S. Jaakkola:
Feature Selection and Dualities in Maximum Entropy Discrimination. UAI 2000: 291-300
1990 – 1999
- 1999
- [j1]Tony Jebara, Ali Azarbayejani, Alex Pentland:
3D structure from 2D motion. IEEE Signal Process. Mag. 16(3): 66-84 (1999) - [c9]Bernt Schiele, Nuria Oliver, Tony Jebara, Alex Pentland:
An Interactive Computer Vision System DyPERS: Dynamic Personal Enhanced Reality System. ICVS 1999: 51-65 - [c8]Tony Jebara, Alex Pentland:
Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour. ICVS 1999: 273-292 - [c7]Tommi S. Jaakkola, Marina Meila, Tony Jebara:
Maximum Entropy Discrimination. NIPS 1999: 470-476 - 1998
- [c6]Tony Jebara, Kenneth B. Russell, Alex Pentland:
Mixtures of Eigen Features for Real-Time Structure from Texture. ICCV 1998: 128-138 - [c5]Baback Moghaddam, Tony Jebara, Alex Pentland:
Efficient MAP/ML similarity matching for visual recognition. ICPR 1998: 876-881 - [c4]Tony Jebara, Alex Pentland:
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm. NIPS 1998: 494-500 - [c3]Baback Moghaddam, Tony Jebara, Alex Pentland:
Bayesian Modeling of Facial Similarity. NIPS 1998: 910-916 - 1997
- [c2]Tony Jebara, Alex Pentland:
Parametrized structure from motion for 3D adaptive feedback tracking of faces. CVPR 1997: 144-150 - [c1]Tony Jebara, Cyrus Eyster, Joshua Weaver, Thad Starner, Alex Pentland:
Stochasticks: Augmenting the Billiards Experience with Probabilistic Vision and Wearable Computers. ISWC 1997: 138-145