Globerson, Amir

101 publications

ICLR 2025 DeciMamba: Exploring the Length Extrapolation Potential of Mamba Assaf Ben-Kish, Itamar Zimerman, Shady Abu-Hussein, Nadav Cohen, Amir Globerson, Lior Wolf, Raja Giryes

NeurIPS 2025 Depth-Width Tradeoffs for Transformers on Graph Tasks Gilad Yehudai, Clayton Sanford, Maya Bechler-Speicher, Orr Fischer, Ran Gilad-Bachrach, Amir Globerson

ICLR 2025 Do LLMs Have Consistent Values? Naama Rozen, Liat Bezalel, Gal Elidan, Amir Globerson, Ella Daniel

AAAI 2025 Teaching Models to Improve on Tape Liat Bezalel, Eyal Orgad, Amir Globerson

ECCV 2024 EgoPet: Egomotion and Interaction Data from an Animal's Perspective Amir Bar, Arya Bakhtiar, Danny L Tran, Antonio Loquercio, Jathushan Rajasegaran, Yann Lecun, Amir Globerson, Trevor Darrell

ECCV 2024 Finding Visual Task Vectors Alberto Hojel, Yutong Bai, Trevor Darrell, Amir Globerson, Amir Bar

ICMLW 2024 Finding Visual Task Vectors Alberto Hojel, Yutong Bai, Trevor Darrell, Amir Globerson, Amir Bar

ICML 2024 Graph Neural Networks Use Graphs When They Shouldn’t Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson

ICML 2024 Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen

WACV 2024 PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers Using Synthetic Scene Data Roei Herzig, Ofir Abramovich, Elad Ben Avraham, Assaf Arbelle, Leonid Karlinsky, Ariel Shamir, Trevor Darrell, Amir Globerson

NeurIPS 2024 Provable Benefits of Complex Parameterizations for Structured State Space Models Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen

ICML 2024 Stochastic Positional Embeddings Improve Masked Image Modeling Amir Bar, Florian Bordes, Assaf Shocher, Mido Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann Lecun

NeurIPS 2024 Stratified Prediction-Powered Inference for Effective Hybrid Evaluation of Language Models Adam Fisch, Joshua Maynez, R. Alex Hofer, Bhuwan Dhingra, Amir Globerson, William W. Cohen

NeurIPS 2024 TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools Avi Caciularu, Alon Jacovi, Eyal Ben-David, Sasha Goldshtein, Tal Schuster, Jonathan Herzig, Gal Elidan, Amir Globerson

AAAI 2024 TREE-G: Decision Trees Contesting Graph Neural Networks Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach

NeurIPS 2024 The Intelligible and Effective Graph Neural Additive Network Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach

NeurIPS 2024 Visual Riddles: A Commonsense and World Knowledge Challenge for Large Vision and Language Models Nitzan Bitton-Guetta, Aviv Slobodkin, Aviya Maimon, Eliya Habba, Royi Rassin, Yonatan Bitton, Idan Szpektor, Amir Globerson, Yuval Elovici

ICLR 2023 Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson

AISTATS 2022 On the Implicit Bias of Gradient Descent for Temporal Extrapolation Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen, Amir Globerson

UAI 2022 Active Learning with Label Comparisons Gal Yona, Shay Moran, Gal Elidan, Amir Globerson

NeurIPS 2022 Bringing Image Scene Structure to Video via Frame-CLIP Consistency of Object Tokens Elad Ben Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson

CVPR 2022 DETReg: Unsupervised Pretraining with Region Priors for Object Detection Amir Bar, Xin Wang, Vadim Kantorov, Colorado J. Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson

ICML 2022 Efficient Learning of CNNs Using Patch Based Features Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Schwartz

CVPR 2022 Object-Region Video Transformers Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson

UAI 2022 On the Inductive Bias of Neural Networks for Learning Read-Once DNFs Ido Bronstein, Alon Brutzkus, Amir Globerson

NeurIPS 2022 Visual Prompting via Image Inpainting Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, Alexei Efros

NeurIPS 2021 A Theoretical Analysis of Fine-Tuning with Linear Teachers Gal Shachaf, Alon Brutzkus, Amir Globerson

UAI 2021 An Optimization and Generalization Analysis for Max-Pooling Networks Alon Brutzkus, Amir Globerson

ICML 2021 Compositional Video Synthesis with Action Graphs Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson

ICCV 2021 Explaining in Style: Training a GAN to Explain a Classifier in StyleSpace Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri

ICML 2021 On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry

ICML 2021 Towards Understanding Learning in Neural Networks with Linear Teachers Roei Sarussi, Alon Brutzkus, Amir Globerson

WACV 2020 Differentiable Scene Graphs Moshiko Raboh, Roei Herzig, Jonathan Berant, Gal Chechik, Amir Globerson

ECCV 2020 Learning Canonical Representations for Scene Graph to Image Generation Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson

ECCV 2020 Learning Object Permanence from Video Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, Gal Chechik

ICLR 2020 Optimal Strategies Against Generative Attacks Roy Mor, Erez Peterfreund, Matan Gavish, Amir Globerson

NeurIPS 2020 Regularizing Towards Permutation Invariance in Recurrent Models Edo Cohen-Karlik, Avichai Ben David, Amir Globerson

AISTATS 2019 Learning Rules-First Classifiers Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan

ICCVW 2019 Spatio-Temporal Action Graph Networks Roei Herzig, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson, Trevor Darrell

ICML 2019 Why Do Larger Models Generalize Better? a Theoretical Perspective via the XOR Problem Alon Brutzkus, Amir Globerson

ICML 2018 Learning to Optimize Combinatorial Functions Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer

NeurIPS 2018 Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson

ICML 2018 Predict and Constrain: Modeling Cardinality in Deep Structured Prediction Nataly Brukhim, Amir Globerson

UAI 2018 Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018 Amir Globerson, Ricardo Silva

ICLR 2018 SGD Learns Over-Parameterized Networks That Provably Generalize on Linearly Separable Data Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz

AISTATS 2018 Semi-Supervised Learning with Competitive Infection Models Nir Rosenfeld, Amir Globerson

COLT 2017 Effective Semisupervised Learning on Manifolds Amir Globerson, Roi Livni, Shai Shalev-Shwartz

ICML 2017 Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs Alon Brutzkus, Amir Globerson

ICML 2017 Learning Infinite Layer Networks Without the Kernel Trick Roi Livni, Daniel Carmon, Amir Globerson

UAI 2017 Learning and Inference with Expectations Amir Globerson

NeurIPS 2017 Robust Conditional Probabilities Yoav Wald, Amir Globerson

AISTATS 2016 Improper Deep Kernels Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson

NeurIPS 2016 Optimal Tagging with Markov Chain Optimization Nir Rosenfeld, Amir Globerson

ICML 2015 How Hard Is Inference for Structured Prediction? Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim

ICML 2014 Discrete Chebyshev Classifiers Elad Eban, Elad Mezuman, Amir Globerson

AISTATS 2014 Efficient Lifting of MAP LP Relaxations Using K-Locality Martin Mladenov, Kristian Kersting, Amir Globerson

ICML 2014 Inferning with High Girth Graphical Models Uri Heinemann, Amir Globerson

AISTATS 2014 Learning Structured Models with the AUC Loss and Its Generalizations Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson

UAI 2014 Lifted Message Passing as Reparametrization of Graphical Models Martin Mladenov, Amir Globerson, Kristian Kersting

ICML 2014 Spectral Regularization for Max-Margin Sequence Tagging Ariadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson

UAI 2014 Tightness Results for Local Consistency Relaxations in Continuous MRFs Yoav Wald, Amir Globerson

ICCV 2013 Higher Order Matching for Consistent Multiple Target Tracking Chetan Arora, Amir Globerson

UAI 2013 Learning Max-Margin Tree Predictors Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson

ICML 2013 The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification Ofir Pele, Ben Taskar, Amir Globerson, Michael Werman

UAI 2013 Tighter Linear Program Relaxations for High Order Graphical Models Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss

ICML 2013 Vanishing Component Analysis Roi Livni, David Lehavi, Sagi Schein, Hila Nachliely, Shai Shalev-Shwartz, Amir Globerson

AISTATS 2012 A Simple Geometric Interpretation of SVM Using Stochastic Adversaries Roi Livni, Koby Crammer, Amir Globerson

NeurIPS 2012 Convergence Rate Analysis of MAP Coordinate Minimization Algorithms Ofer Meshi, Amir Globerson, Tommi S. Jaakkola

ICML 2012 Learning the Experts for Online Sequence Prediction Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson

ECML-PKDD 2011 An Alternating Direction Method for Dual MAP LP Relaxation Ofer Meshi, Amir Globerson

UAI 2011 What Cannot Be Learned with Bethe Approximations Uri Heinemann, Amir Globerson

AISTATS 2010 Learning Bayesian Network Structure Using LP Relaxations Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila

ICML 2010 Learning Efficiently with Approximate Inference via Dual Losses Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson

NeurIPS 2010 More Data Means Less Inference: A Pseudo-Max Approach to Structured Learning David Sontag, Ofer Meshi, Amir Globerson, Tommi S. Jaakkola

NeurIPS 2009 An LP View of the M-Best MAP Problem Menachem Fromer, Amir Globerson

UAI 2009 Convergent Message Passing Algorithms - A Unifying View Talya Meltzer, Amir Globerson, Yair Weiss

UAI 2009 Convexifying the Bethe Free Energy Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman

NeurIPS 2008 Clusters and Coarse Partitions in LP Relaxations David Sontag, Amir Globerson, Tommi S. Jaakkola

JMLR 2008 Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett

UAI 2008 Tightening LP Relaxations for MAP Using Message Passing David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss

AISTATS 2007 Approximate Inference Using Conditional Entropy Decompositions Amir Globerson, Tommi Jaakkola

UAI 2007 Convergent Propagation Algorithms via Oriented Trees Amir Globerson, Tommi S. Jaakkola

NeurIPS 2007 Convex Learning with Invariances Choon H. Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola

JMLR 2007 Euclidean Embedding of Co-Occurrence Data Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby

ICML 2007 Exponentiated Gradient Algorithms for Log-Linear Structured Prediction Amir Globerson, Terry Koo, Xavier Carreras, Michael Collins

NeurIPS 2007 Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations Amir Globerson, Tommi S. Jaakkola

AISTATS 2007 Visualizing Pairwise Similarity via Semidefinite Programming Amir Globerson, Sam Roweis

NeurIPS 2006 Approximate Inference Using Planar Graph Decomposition Amir Globerson, Tommi S. Jaakkola

UAI 2006 Discriminative Learning via Semidefinite Probabilistic Models Koby Crammer, Amir Globerson

AAAI 2006 Embedding Heterogeneous Data Using Statistical Models Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby

ICML 2006 Nightmare at Test Time: Robust Learning by Feature Deletion Amir Globerson, Sam T. Roweis

AISTATS 2005 Distributed Latent Variable Models of Lexical Co-Occurrences John Blitzer, Amir Globerson, Fernando Pereira

JMLR 2005 Information Bottleneck for Gaussian Variables Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss

NeurIPS 2005 Metric Learning by Collapsing Classes Amir Globerson, Sam T. Roweis

NeurIPS 2004 Euclidean Embedding of Co-Occurrence Data Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby

UAI 2004 The Minimum Information Principle for Discriminative Learning Amir Globerson, Naftali Tishby

NeurIPS 2003 Information Bottleneck for Gaussian Variables Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss

UAI 2003 Sufficient Dimensionality Reduction with Irrelevance Statistics Amir Globerson, Gal Chechik, Naftali Tishby

AAAI 2002 Most Informative Dimension Reduction Amir Globerson, Naftali Tishby

ICML 2002 Sufficient Dimensionality Reduction - A Novel Analysis Method Amir Globerson, Naftali Tishby

NeurIPS 2001 Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway Gal Chechik, Amir Globerson, M. J. Anderson, E. D. Young, Israel Nelken, Naftali Tishby