Discovering Hidden Variables: A Structure-Based Approach. - Gal Elidan; Noam Lotner; Nir Friedman; Daphne Koller (NIPS, 2000)

Inferring subnetworks from perturbed expression profiles. - Dana Pe'er; Aviv Regev; Gal Elidan; Nir Friedman (ISMB (Supplement of Bioinformatics), 2001)

Learning the Dimensionality of Hidden Variables. - Gal Elidan; Nir Friedman (UAI, 2001)

Data Perturbation for Escaping Local Maxima in Learning. - Gal Elidan; Matan Ninio; Nir Friedman; Dale Schuurmans (AAAI/IAAI, 2002)

Modeling dependencies in protein-DNA binding sites. - Yoseph Barash; Gal Elidan; Nir Friedman; Tommy Kaplan (RECOMB, 2003)

The Information Bottleneck EM Algorithm. - Gal Elidan; Nir Friedman (UAI, 2003)

"Ideal Parent" Structure Learning for Continuous Variable Networks. - Iftach Nachman; Gal Elidan; Nir Friedman (UAI, 2004)

Learning hidden variables in probabilistic graphical models (למידת משתנים חבויים במודלים גרפיים הסתברותיים.). - Gal Elidan (2004)

Towards an Integrated Protein-Protein Interaction Network. - Ariel Jaimovich; Gal Elidan; Hanah Margalit; Nir Friedman (RECOMB, 2005)

CIS: compound importance sampling method for protein-DNA binding site p-value estimation. - Yoseph Barash; Gal Elidan; Tommy Kaplan; Nir Friedman (Bioinform., 2005)

Learning Hidden Variable Networks: The Information Bottleneck Approach. - Gal Elidan; Nir Friedman (J. Mach. Learn. Res., 2005)

Learning Object Shape: From Drawings to Images. - Gal Elidan; Geremy Heitz; Daphne Koller (CVPR (2), 2006)

Max-margin classification of incomplete data. - Gal Chechik; Geremy Heitz; Gal Elidan; Pieter Abbeel; Daphne Koller (NIPS, 2006)

Using Combinatorial Optimization within Max-Product Belief Propagation. - John C. Duchi; Daniel Tarlow; Gal Elidan; Daphne Koller (NIPS, 2006)

Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing. - Gal Elidan; Ian McGraw; Daphne Koller (UAI, 2006)

Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach. - Ariel Jaimovich; Gal Elidan; Hanah Margalit; Nir Friedman (J. Comput. Biol., 2006)

"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. - Gal Elidan; Iftach Nachman; Nir Friedman (J. Mach. Learn. Res., 2007)

Learning Bounded Treewidth Bayesian Networks. - Gal Elidan; Stephen Gould (NIPS, 2008)

Convex Point Estimation using Undirected Bayesian Transfer Hierarchies. - Gal Elidan; Benjamin Packer; Geremy Heitz; Daphne Koller (UAI, 2008)

Multi-Class Segmentation with Relative Location Prior. - Stephen Gould; Jim Rodgers; David Cohen; Gal Elidan; Daphne Koller (Int. J. Comput. Vis., 2008)

Max-margin Classification of Data with Absent Features. - Gal Chechik; Geremy Heitz; Gal Elidan; Pieter Abbeel; Daphne Koller (J. Mach. Learn. Res., 2008)

Shape-Based Object Localization for Descriptive Classification. - Geremy Heitz; Gal Elidan; Benjamin Packer; Daphne Koller (Int. J. Comput. Vis., 2009)

Copula Bayesian Networks. - Gal Elidan (NIPS, 2010)

Inference-less Density Estimation using Copula Bayesian Networks. - Gal Elidan (UAI, 2010)

FastInf: An Efficient Approximate Inference Library. - Ariel Jaimovich; Ofer Meshi; Ian McGraw; Gal Elidan (J. Mach. Learn. Res., 2010)

Bagged Structure Learning of Bayesian Network. - Gal Elidan (AISTATS, 2011)

A Diverse Dirichlet Process Ensemble for Unsupervised Induction of Syntactic Categories. - Roi Reichart; Gal Elidan; Ari Rappoport (COLING, 2012)

Nonparanormal Belief Propagation (NPNBP). - Gal Elidan; Cobi Cario (NIPS, 2012)

Copula Network Classifiers (CNCs). - Gal Elidan (AISTATS, 2012)

Lightning-speed Structure Learning of Nonlinear Continuous Networks. - Gal Elidan (AISTATS, 2012)

Dynamic Copula Networks for Modeling Real-valued Time Series. - Elad Eban; Gideon Rothschild; Adi Mizrahi; Israel Nelken; Gal Elidan (AISTATS, 2013)

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

Speedy Model Selection (SMS) for Copula Models. - Yaniv Tenzer; Gal Elidan (UAI, 2013)

HELM: Highly Efficient Learning of Mixed copula networks. - Yaniv Tenzer; Gal Elidan (UAI, 2014)

Quaternion structured paranormal distributions. - Yonatan Woodbridge; Gal Elidan; Ami Wiesel (ACSSC, 2016)

Improper Deep Kernels. - Uri Heinemann; Roi Livni; Elad Eban; Gal Elidan; Amir Globerson (AISTATS, 2016)

Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables. - Yaniv Tenzer; Gal Elidan (AISTATS, 2016)

Signal detection in para complex normal noise. - Yonatan Woodbridge; Gal Elidan; Ami Wiesel (ICASSP, 2016)

Large-scale Learning With Global Non-Decomposable Objectives. - Elad Eban; Mariano Schain; Ariel Gordon; Rif Saurous; Gal Elidan (CoRR, 2016)

Scalable Learning of Non-Decomposable Objectives. - Elad Eban; Mariano Schain; Alan Mackey; Ariel Gordon; Ryan Rifkin; Gal Elidan (AISTATS, 2017)

Logistic Markov Decision Processes. - Martin Mladenov; Craig Boutilier; Dale Schuurmans; Ofer Meshi; Gal Elidan; Tyler Lu (IJCAI, 2017)

Signal Detection in Complex Structured Para Normal Noise. - Yonatan Woodbridge; Gal Elidan; Ami Wiesel (IEEE Trans. Signal Process., 2017)

Learning with Rules. - Deborah Cohen; Amit Daniely; Amir Globerson; Gal Elidan (CoRR, 2018)

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

Robust multitask Elliptical Regression (ROMER). - Nofar Noy; Yoav Wald; Gal Elidan; Ami Wiesel (CAMSAP, 2019)

Globally Optimal Learning for Structured Elliptical Losses. - Yoav Wald; Nofar Noy; Gal Elidan; Ami Wiesel (NeurIPS, 2019)

Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many. - Yotam Gigi; Gal Elidan; Avinatan Hassidim; Yossi Matias; Zach Moshe; Sella Nevo; Guy Shalev; Ami Wiesel (CoRR, 2019)

ML for Flood Forecasting at Scale. - Sella Nevo; Vova Anisimov; Gal Elidan; Ran El-Yaniv; Pete Giencke; Yotam Gigi; Avinatan Hassidim; Zach Moshe; Mor Schlesinger; Guy Shalev; Ajai Tirumali; Ami Wiesel; et al. (CoRR, 2019)

Spectral Algorithm for Low-rank Multitask Regression. - Yotam Gigi; Ami Wiesel; Sella Nevo; Gal Elidan; Avinatan Hassidim; Yossi Matias (CoRR, 2019)

Improved Detection of Adversarial Attacks via Penetration Distortion Maximization. - Shai Rozenberg; Gal Elidan; Ran El-Yaniv (CoRR, 2019)

Unmixing K-Gaussians With Application to Hyperspectral Imaging. - Yonatan Woodbridge; Uri Okun; Gal Elidan; Ami Wiesel (IEEE Trans. Geosci. Remote. Sens., 2019)

Spectral Algorithm for Shared Low-rank Matrix Regressions. - Yotam Gigi; Sella Nevo; Gal Elidan; Avinatan Hassidim; Yossi Matias; Ami Wiesel (SAM, 2020)

Dynamic Composition for Conversational Domain Exploration. - Idan Szpektor; Deborah Cohen; Gal Elidan; Michael Fink; Avinatan Hassidim; Orgad Keller; Sayali Kulkarni; Eran Ofek; Sagie Pudinsky; Asaf Revach; Shimi Salant; Yossi Matias (WWW, 2020)

Normalizing Flow Regression. - Yonatan Woodbridge; Gal Elidan; Ami Wiesel (CoRR, 2020)

DNF-Net: A Neural Architecture for Tabular Data. - Ami Abutbul; Gal Elidan; Liran Katzir 001; Ran El-Yaniv (CoRR, 2020)

HydroNets: Leveraging River Structure for Hydrologic Modeling. - Zach Moshe; Asher Metzger; Gal Elidan; Frederik Kratzert; Sella Nevo; Ran El-Yaniv (CoRR, 2020)

ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach. - Sella Nevo; Gal Elidan; Avinatan Hassidim; Guy Shalev; Oren Gilon; Grey Nearing; Yossi Matias (CoRR, 2020)

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 (ICCV, 2021)

Net-DNF: Effective Deep Modeling of Tabular Data. - Liran Katzir 001; Gal Elidan; Ran El-Yaniv (ICLR, 2021)

Solving Sokoban with Forward-Backward Reinforcement Learning. - Yaron Shoham; Gal Elidan (SOCS, 2021)

Flood forecasting with machine learning models in an operational framework. - Sella Nevo; Efrat Morin; Adi Gerzi Rosenthal; Asher Metzger; Chen Barshai; Dana Weitzner; Dafi Voloshin; Frederik Kratzert; Gal Elidan; Gideon Dror; Gregory Begelman; Grey Nearing; et al. (CoRR, 2021)

Convex Nonparanormal Regression. - Yonatan Woodbridge; Gal Elidan; Ami Wiesel (IEEE Signal Process. Lett., 2021)

Active learning with label comparisons. - Gal Yona; Shay Moran; Gal Elidan; Amir Globerson (UAI, 2022)

Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning. - Deborah Cohen; Moonkyung Ryu; Yinlam Chow; Orgad Keller; Ido Greenberg; Avinatan Hassidim; Michael Fink; Yossi Matias; Idan Szpektor; Craig Boutilier; Gal Elidan (CoRR, 2022)

Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment. - Roni Rabin; Alexandre Djerbetian; Roee Engelberg; Lidan Hackmon; Gal Elidan; Reut Tsarfaty; Amir Globerson (ACL (2), 2023)

Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. - Paul Roit; Johan Ferret; Lior Shani; Roee Aharoni; Geoffrey Cideron; Robert Dadashi; Matthieu Geist; Sertan Girgin; Léonard Hussenot; Orgad Keller; Nikola Momchev; Sabela Ramos Garea; et al. (ACL (1), 2023)

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 (NeurIPS, 2024)

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach. - Irina Jurenka; Markus Kunesch; Kevin R. McKee; Daniel Gillick; Shaojian Zhu; Sara Wiltberger; Shubham Milind Phal; Katherine L. Hermann; Daniel Kasenberg; Avishkar Bhoopchand; Ankit Anand; Miruna Pîslar; et al. (CoRR, 2024)

Do LLMs have Consistent Values? - Naama Rozen; Gal Elidan; Amir Globerson; Ella Daniel (CoRR, 2024)

Do LLMs have Consistent Values? - Naama Rozen; Liat Bezalel; Gal Elidan; Amir Globerson; Ella Daniel (ICLR, 2025)

Evaluating Gemini in an arena for learning. - Abhinit Modi; Aditya Srikanth Veerubhotla; Aliya Rysbek; Andrea Huber; Ankit Anand; Avishkar Bhoopchand; Brett Wiltshire; Daniel Gillick; Daniel Kasenberg; Eleni Sgouritsa; Gal Elidan; Hengrui Liu; et al. (CoRR, 2025)