Tamir Hazan

发表

Tightening Fractional Covering Upper Bounds on the Partition Function for High-Order Region Graphs

Tamir Hazan, Jian Peng, Amnon Shashua, 2012, UAI.

Psychological Forest: Predicting Human Behavior

Moshe Tennenholtz, Tamir Hazan, Ori Plonsky, 2016, AAAI.

Direct Loss Minimization for Structured Prediction

Tamir Hazan, David A. McAllester, Joseph Keshet, 2010, NIPS.

Co-segmentation for space-time co-located collections

Daniel Cohen-Or, Tamir Hazan, Johannes Kopf, 2017, The Visual Computer.

High-Order Attention Models for Visual Question Answering

Tamir Hazan, Alexander G. Schwing, Idan Schwartz, 2017, NIPS.

Sparse image coding using a 3D non-negative tensor factorization

Tamir Hazan, Amnon Shashua, Simon Polak, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

A Simple Baseline for Audio-Visual Scene-Aware Dialog

Tamir Hazan, Alexander G. Schwing, Idan Schwartz, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning pdf

Shie Mannor, Tamir Hazan, Hanlin Tang, 2020, ArXiv.

Continuous Markov Random Fields for Robust Stereo Estimation pdf

Tamir Hazan, Raquel Urtasun, David A. McAllester, 2012, ECCV.

A Formal Approach to Explainability

Lior Wolf, Tamir Hazan, Tomer Galanti, 2019, AIES.

On Measure Concentration of Random Maximum A-Posteriori Perturbations

Anand D. Sarwate, Tommi S. Jaakkola, Tamir Hazan, 2013, ICML.

Steps Toward Deep Kernel Methods from Infinite Neural Networks pdf

Tommi S. Jaakkola, Tamir Hazan, T. Jaakkola, 2015, ArXiv.

Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization

Tamir Hazan, Amnon Shashua, Ron Zass, 2006, ECCV.

Congruency-Based Reranking

Nachum Dershowitz, Tamir Hazan, Lior Wolf, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Blending Learning and Inference in Structured Prediction

Tamir Hazan, Raquel Urtasun, Alexander Schwing, 2012, 1210.2346.

Following the Perturbed Leader for Online Structured Learning

Tamir Hazan, Alon Cohen, Tamir Hazan, 2015, ICML.

A Parallel Decomposition Solver for SVM: Distributed dual ascend using Fenchel Duality

Tamir Hazan, Amnon Shashua, Amit Man, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Efficient Structured Prediction with Latent Variables for General Graphical Models

Marc Pollefeys, Tamir Hazan, Raquel Urtasun, 2012, ICML.

K-hyperplane Hinge-Minimax Classifier

Tamir Hazan, Daniel Keren, Margarita Osadchy, 2015, ICML.

Stochastic proximal linear method for structured non-convex problems

Tamir Hazan, Shoham Sabach, Sergey Voldman, 2020, Optim. Methods Softw..

Computational Education using Latent Structured Prediction

Markus H. Gross, Tamir Hazan, Alexander G. Schwing, 2014, AISTATS.

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces pdf

Tamir Hazan, Daniel Tarlow, Nicolas Heess, 2019, NeurIPS.

On the generalization of bayesian deep nets for multi-class classification pdf

Tamir Hazan, Yossi Adi, Alex Schwing, 2020, ArXiv.

Efficient Training of Structured SVMs via Soft Constraints

Tamir Hazan, Ofer Meshi, Nathan Srebro, 2015, AISTATS.

Algebraic Set Kernels with Application to Inference Over Local Image Representations

Tamir Hazan, Amnon Shashua, A. Shashua, 2004, NIPS.

Modeling Appearances with Low-Rank SVM

Lior Wolf, Tamir Hazan, Hueihan Jhuang, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies

Tamir Hazan, Amnon Shashua, A. Shashua, 2008, UAI.

Constraints Based Convex Belief Propagation

Tamir Hazan, Alexander G. Schwing, Kevin Gimpel, 2016, NIPS.

On the Partition Function and Random Maximum A-Posteriori Perturbations

Tommi S. Jaakkola, Tamir Hazan, T. Jaakkola, 2012, ICML.

Hinge-Minimax Learner for the Ensemble of Hyperplanes

Tamir Hazan, Margarita Osadchy, Dolev Raviv, 2018, J. Mach. Learn. Res..

Non-negative tensor factorization with applications to statistics and computer vision

Tamir Hazan, Amnon Shashua, A. Shashua, 2005, ICML.

Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions

Subhransu Maji, Tommi S. Jaakkola, Tamir Hazan, 2013, NIPS.

Learning with Maximum A-Posteriori Perturbation Models

Tommi S. Jaakkola, Tamir Hazan, Andreea Gane, 2014, AISTATS.

Factor Graph Attention

Tamir Hazan, Alexander G. Schwing, Seunghak Yu, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations

Subhransu Maji, Tommi S. Jaakkola, Tamir Hazan, 2013, NIPS.

Efficient Learning of Structured Predictors in General Graphical Models pdf

Tamir Hazan, Raquel Urtasun, 2012, ArXiv.

Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm

Marc Pollefeys, Tamir Hazan, Raquel Urtasun, 2014, ICML.

Online Learning with Feedback Graphs Without the Graphs

Tamir Hazan, Alon Cohen, Tomer Koren, 2016, ICML 2016.

Direct Optimization through arg max for Discrete Variational Auto-Encoder

Tommi S. Jaakkola, Tamir Hazan, Andreea Gane, 2018, NeurIPS.

Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts

Christoph Schnörr, Tamir Hazan, Bogdan Savchynskyy, 2015, SSVM.

pLSA for Sparse Arrays With Tsallis Pseudo-Additive Divergence: Noise Robustness and Algorithm

Tamir Hazan, Amnon Shashua, Roee Hardoon, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Perturbation Based Learning for Structured NLP Tasks with Application to Dependency Parsing

Tamir Hazan, Roi Reichart, Amichay Doitch, 2019, Transactions of the Association for Computational Linguistics.

Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins

Marc Pollefeys, Tamir Hazan, Raquel Urtasun, 2012, NIPS.

Gravity Direction Estimation and Heading Determination for Pedestrian Navigation

Tamir Hazan, Itzik Klein, Adi Manos, 2018, 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

High Dimensional Inference With Random Maximum A-Posteriori Perturbations

Subhransu Maji, Anand D. Sarwate, Tommi S. Jaakkola, 2016, IEEE Transactions on Information Theory.

A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction

Tamir Hazan, Raquel Urtasun, R. Urtasun, 2010, NIPS.

Approximated Structured Prediction for Learning Large Scale Graphical Models pdf

Tamir Hazan, Raquel Urtasun, 2010, ArXiv.

Norm-Product Belief Propagation: Primal-Dual Message-Passing for Approximate Inference

Tamir Hazan, Amnon Shashua, A. Shashua, 2009, IEEE Transactions on Information Theory.

Tight Bounds for Bandit Combinatorial Optimization

Tamir Hazan, Alon Cohen, Tomer Koren, 2017, COLT.

Gravity-Based Methods for Heading Computation in Pedestrian Dead Reckoning

Tamir Hazan, Itzik Klein, Adi Manos, 2019, Sensors.

Active Boundary Annotation using Random MAP Perturbations

Subhransu Maji, Tommi S. Jaakkola, Tamir Hazan, 2014, AISTATS.

PAC-Bayesian approach for minimization of phoneme error rate

Tamir Hazan, David A. McAllester, Joseph Keshet, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization pdf

Tamir Hazan, Hedda Cohen Indelman, 2020, ArXiv.

Blending Learning and Inference in Conditional Random Fields

Tamir Hazan, Raquel Urtasun, Alexander G. Schwing, 2016, J. Mach. Learn. Res..

Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies pdf

Tamir Hazan, Alexander Schwing, Idan Schwartz, 2020, NeurIPS.

Efficient structured prediction for 3D indoor scene understanding

Marc Pollefeys, Tamir Hazan, Raquel Urtasun, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

PAC-Bayesian Neural Network Bounds

Tamir Hazan, Alexander G. Schwing, Yossi Adi, 2019 .

Approximate Inference by Intersecting Semidefinite Bound and Local Polytope

Tamir Hazan, Jian Peng, Nathan Srebro, 2012, AISTATS.

Distributed message passing for large scale graphical models

Marc Pollefeys, Tamir Hazan, Raquel Urtasun, 2011, CVPR 2011.

Multicuts and Perturb & MAP for Probabilistic Graph Clustering

Christoph Schnörr, Tamir Hazan, Bogdan Savchynskyy, 2016, Journal of Mathematical Imaging and Vision.

Active Congruency-Based Reranking

Nachum Dershowitz, Tamir Hazan, Lior Wolf, 2016, Front. Digit. Humanit..

Perturbations, Optimization, and Statistics

George Papandreou, Tamir Hazan, Daniel Tarlow, 2016 .

Generalized Planning With Deep Reinforcement Learning pdf

Erez Karpas, Tamir Hazan, Or Rivlin, 2020, ArXiv.

A theoretical framework for deep transfer learning

Lior Wolf, Tamir Hazan, Tomer Galanti, 2016 .

Mixing between the Cross Entropy and the Expectation Loss Terms pdf

Lior Wolf, Tamir Hazan, Barak Battash, 2021, ArXiv.

Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy pdf

Bhiksha Raj, Tamir Hazan, Joseph Keshet, 2021, ArXiv.

Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization pdf

Tamir Hazan, Hedda Cohen Indelman, 2021, ICML.

Video and Text Matching with Conditioned Embeddings pdf

Lior Wolf, Tamir Hazan, Idan Schwartz, 2021, ArXiv.

Visual Navigation with Spatial Attention

Tamir Hazan, Ayellet Tal, Bar Mayo, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Latent Space Explanation by Intervention pdf

Tamir Hazan, Idan Schwartz, Guy Lorberbom, 2021, AAAI.

Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning pdf

Shie Mannor, Tamir Hazan, Hanlin Tang, 2021, ICLR.

Learning Generalized Gumbel-max Causal Mechanisms pdf

Tamir Hazan, Daniel Tarlow, Chris J. Maddison, 2021, NeurIPS.

Distributed Structured Prediction for Big Data

Marc Pollefeys, Tamir Hazan, Alexander G. Schwing, 2012 .

Analysis of L2-loss for Probabilistically valid Factorizations under General Additive Noise

Tamir Hazan, Amnon Shashua, 2007 .