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2020 – today
- 2024
[i28]Avi Caciularu, Alon Jacovi, Eyal Ben-David, Sasha Goldshtein, Tal Schuster, Jonathan Herzig, Gal Elidan, Amir Globerson:
TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools. CoRR abs/2406.03618 (2024)- [i27]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, Stephanie Chan, Lisa Wang, Jennifer She, Parsa Mahmoudieh, Aliya Rysbek, Wei-Jen Ko, Andrea Huber, Brett Wiltshire, Gal Elidan, Roni Rabin, Jasmin Rubinovitz, Amit Pitaru, Mac McAllister, Julia Wilkowski, David Choi, Roee Engelberg, Lidan Hackmon, Adva Levin, Rachel Griffin, Michael Sears, Filip Bar, Mia Mesar, Mana Jabbour, Arslan Chaudhry, James Cohan, Sridhar Thiagarajan, Nir Levine, Ben Brown, Dilan Görür, Svetlana Grant, Rachel Hashimshoni, Laura Weidinger, Jieru Hu, Dawn Chen, Kuba Dolecki, Canfer Akbulut, Maxwell L. Bileschi, Laura Culp, Wen-Xin Dong, Nahema Marchal, Kelsie Van Deman, Hema Bajaj Misra, Michael Duah, Moran Ambar, Avi Caciularu, Sandra Lefdal, Christopher Summerfield, James An, Pierre-Alexandre Kamienny, Abhinit Mohdi, Theofilos Strinopoulos, Annie Hale, Wayne Anderson, Luis C. Cobo, Niv Efron, Muktha Ananda, Shakir Mohamed, Maureen Heymans, Zoubin Ghahramani, Yossi Matias, Ben Gomes, Lila Ibrahim:
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach. CoRR abs/2407.12687 (2024) - [i26]Naama Rozen, Gal Elidan, Amir Globerson, Ella Daniel:
Do LLMs have Consistent Values? CoRR abs/2407.12878 (2024) - 2023
- [c43]Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon, Gal Elidan, Reut Tsarfaty, Amir Globerson:
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment. ACL (2) 2023: 215-227 - [c42]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, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor:
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. ACL (1) 2023: 6252-6272 - [i25]Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor:
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. CoRR abs/2306.00186 (2023) - [i24]Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon, Gal Elidan, Reut Tsarfaty, Amir Globerson:
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment. CoRR abs/2307.03319 (2023) - 2022
- [c41]Gal Yona, Shay Moran, Gal Elidan, Amir Globerson:
Active learning with label comparisons. UAI 2022: 2289-2298 - [i23]Gal Yona, Shay Moran, Gal Elidan, Amir Globerson:
Active Learning with Label Comparisons. CoRR abs/2204.04670 (2022) - [i22]Deborah Cohen, Moonkyung Ryu, Yinlam Chow, Orgad Keller, Ido Greenberg, Avinatan Hassidim, Michael Fink, Yossi Matias, Idan Szpektor, Craig Boutilier, Gal Elidan:
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning. CoRR abs/2208.02294 (2022) - 2021
- [j11]Yonatan Woodbridge
, Gal Elidan, Ami Wiesel:
Convex Nonparanormal Regression. IEEE Signal Process. Lett. 28: 1680-1684 (2021) - [c40]Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri:
Explaining in Style: Training a GAN to explain a classifier in StyleSpace. ICCV 2021: 673-682 - [c39]Liran Katzir, Gal Elidan, Ran El-Yaniv:
Net-DNF: Effective Deep Modeling of Tabular Data. ICLR 2021 - [c38]Yaron Shoham, Gal Elidan:
Solving Sokoban with Forward-Backward Reinforcement Learning. SOCS 2021: 191-193 - [i21]Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri:
Explaining in Style: Training a GAN to explain a classifier in StyleSpace. CoRR abs/2104.13369 (2021) - [i20]Yaron Shoham, Gal Elidan:
Solving Sokoban with forward-backward reinforcement learning. CoRR abs/2105.01904 (2021) - [i19]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, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, Yossi Matias:
Flood forecasting with machine learning models in an operational framework. CoRR abs/2111.02780 (2021) - 2020
- [c37]Yotam Gigi, Sella Nevo, Gal Elidan, Avinatan Hassidim, Yossi Matias, Ami Wiesel:
Spectral Algorithm for Shared Low-rank Matrix Regressions. SAM 2020: 1-5 - [c36]Idan Szpektor, Deborah Cohen, Gal Elidan, Michael Fink, Avinatan Hassidim, Orgad Keller, Sayali Kulkarni, Eran Ofek, Sagie Pudinsky, Asaf Revach, Shimi Salant, Yossi Matias:
Dynamic Composition for Conversational Domain Exploration. WWW 2020: 872-883 - [i18]Yonatan Woodbridge, Gal Elidan, Ami Wiesel:
Normalizing Flow Regression. CoRR abs/2004.10255 (2020) - [i17]Ami Abutbul, Gal Elidan, Liran Katzir, Ran El-Yaniv:
DNF-Net: A Neural Architecture for Tabular Data. CoRR abs/2006.06465 (2020) - [i16]Zach Moshe, Asher Metzger, Gal Elidan, Frederik Kratzert, Sella Nevo, Ran El-Yaniv:
HydroNets: Leveraging River Structure for Hydrologic Modeling. CoRR abs/2007.00595 (2020) - [i15]Sella Nevo, Gal Elidan, Avinatan Hassidim, Guy Shalev, Oren Gilon, Grey Nearing, Yossi Matias:
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach. CoRR abs/2012.00671 (2020)
2010 – 2019
- 2019
- [j10]Yonatan Woodbridge, Uri Okun, Gal Elidan, Ami Wiesel:
Unmixing K-Gaussians With Application to Hyperspectral Imaging. IEEE Trans. Geosci. Remote. Sens. 57(9): 7281-7293 (2019) - [c35]Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan:
Learning Rules-First Classifiers. AISTATS 2019: 1398-1406 - [c34]Nofar Noy, Yoav Wald, Gal Elidan, Ami Wiesel:
Robust multitask Elliptical Regression (ROMER). CAMSAP 2019: 261-265 - [c33]Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel:
Globally Optimal Learning for Structured Elliptical Losses. NeurIPS 2019: 13488-13497 - [i14]Yotam Gigi, Gal Elidan, Avinatan Hassidim, Yossi Matias, Zach Moshe, Sella Nevo, Guy Shalev, Ami Wiesel:
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many. CoRR abs/1901.00786 (2019) - [i13]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, Oleg Zlydenko, Yossi Matias:
ML for Flood Forecasting at Scale. CoRR abs/1901.09583 (2019) - [i12]Yotam Gigi, Ami Wiesel, Sella Nevo, Gal Elidan, Avinatan Hassidim, Yossi Matias:
Spectral Algorithm for Low-rank Multitask Regression. CoRR abs/1910.12204 (2019) - [i11]Shai Rozenberg, Gal Elidan, Ran El-Yaniv:
Improved Detection of Adversarial Attacks via Penetration Distortion Maximization. CoRR abs/1911.00870 (2019) - 2018
- [i10]Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan:
Learning with Rules. CoRR abs/1803.03155 (2018) - 2017
- [j9]Yonatan Woodbridge, Gal Elidan, Ami Wiesel:
Signal Detection in Complex Structured Para Normal Noise. IEEE Trans. Signal Process. 65(9): 2306-2316 (2017) - [c32]Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Ryan Rifkin, Gal Elidan:
Scalable Learning of Non-Decomposable Objectives. AISTATS 2017: 832-840 - [c31]Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu:
Logistic Markov Decision Processes. IJCAI 2017: 2486-2493 - [e1]Gal Elidan, Kristian Kersting, Alexander Ihler:
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017. AUAI Press 2017 [contents] - 2016
- [c30]Yonatan Woodbridge, Gal Elidan, Ami Wiesel:
Quaternion structured paranormal distributions. ACSSC 2016: 815-819 - [c29]Yaniv Tenzer, Gal Elidan:
Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables. AISTATS 2016: 222-230 - [c28]Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson:
Improper Deep Kernels. AISTATS 2016: 1159-1167 - [c27]Yonatan Woodbridge, Gal Elidan, Ami Wiesel:
Signal detection in para complex normal noise. ICASSP 2016: 4274-4278 - [i9]Elad Eban, Mariano Schain, Ariel Gordon, Rif Saurous, Gal Elidan:
Large-scale Learning With Global Non-Decomposable Objectives. CoRR abs/1608.04802 (2016) - 2014
- [c26]Yaniv Tenzer, Gal Elidan:
HELM: Highly Efficient Learning of Mixed copula networks. UAI 2014: 790-799 - 2013
- [c25]Elad Eban, Gideon Rothschild, Adi Mizrahi, Israel Nelken, Gal Elidan:
Dynamic Copula Networks for Modeling Real-valued Time Series. AISTATS 2013: 247-255 - [c24]Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson:
Learning Max-Margin Tree Predictors. UAI 2013 - [c23]Yaniv Tenzer, Gal Elidan:
Speedy Model Selection (SMS) for Copula Models. UAI 2013 - [i8]Gal Elidan, Nir Friedman:
Learning the Dimensionality of Hidden Variables. CoRR abs/1301.2269 (2013) - [i7]Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson:
Learning Max-Margin Tree Predictors. CoRR abs/1309.6847 (2013) - [i6]Yaniv Tenzer, Gal Elidan:
Speedy Model Selection (SMS) for Copula Models. CoRR abs/1309.6867 (2013) - 2012
- [c22]Roi Reichart, Gal Elidan, Ari Rappoport:
A Diverse Dirichlet Process Ensemble for Unsupervised Induction of Syntactic Categories. COLING 2012: 2307-2324 - [c21]Gal Elidan, Cobi Cario:
Nonparanormal Belief Propagation (NPNBP). NIPS 2012: 908-916 - [c20]Gal Elidan:
Copula Network Classifiers (CNCs). AISTATS 2012: 346-354 - [c19]Gal Elidan:
Lightning-speed Structure Learning of Nonlinear Continuous Networks. AISTATS 2012: 355-363 - [i5]Gal Elidan:
Inference-less Density Estimation using Copula Bayesian Networks. CoRR abs/1203.3476 (2012) - [i4]Gal Elidan, Benjamin Packer, Geremy Heitz, Daphne Koller:
Convex Point Estimation using Undirected Bayesian Transfer Hierarchies. CoRR abs/1206.3252 (2012) - [i3]Gal Elidan, Ian McGraw, Daphne Koller:
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing. CoRR abs/1206.6837 (2012) - [i2]Iftach Nachman, Gal Elidan, Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Networks. CoRR abs/1207.4133 (2012) - [i1]Gal Elidan, Nir Friedman:
The Information Bottleneck EM Algorithm. CoRR abs/1212.2460 (2012) - 2011
- [c18]Gal Elidan:
Bagged Structure Learning of Bayesian Network. AISTATS 2011: 251-259 - 2010
- [j8]Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan:
FastInf: An Efficient Approximate Inference Library. J. Mach. Learn. Res. 11: 1733-1736 (2010) - [c17]Gal Elidan:
Copula Bayesian Networks. NIPS 2010: 559-567 - [c16]Gal Elidan:
Inference-less Density Estimation using Copula Bayesian Networks. UAI 2010: 151-159
2000 – 2009
- 2009
- [j7]Geremy Heitz, Gal Elidan, Benjamin Packer, Daphne Koller:
Shape-Based Object Localization for Descriptive Classification. Int. J. Comput. Vis. 84(1): 40-62 (2009) - 2008
- [j6]Stephen Gould, Jim Rodgers, David Cohen, Gal Elidan, Daphne Koller:
Multi-Class Segmentation with Relative Location Prior. Int. J. Comput. Vis. 80(3): 300-316 (2008) - [j5]Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
Max-margin Classification of Data with Absent Features. J. Mach. Learn. Res. 9: 1-21 (2008) - [c15]Gal Elidan, Stephen Gould:
Learning Bounded Treewidth Bayesian Networks. NIPS 2008: 417-424 - [c14]Geremy Heitz, Gal Elidan, Benjamin Packer, Daphne Koller:
Shape-Based Object Localization for Descriptive Classification. NIPS 2008: 633-640 - [c13]Gal Elidan, Benjamin Packer, Geremy Heitz, Daphne Koller:
Convex Point Estimation using Undirected Bayesian Transfer Hierarchies. UAI 2008: 179-186 - 2007
- [j4]Gal Elidan, Iftach Nachman, Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. J. Mach. Learn. Res. 8: 1799-1833 (2007) - 2006
- [j3]Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman:
Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach. J. Comput. Biol. 13(2): 145-164 (2006) - [c12]Gal Elidan, Geremy Heitz, Daphne Koller:
Learning Object Shape: From Drawings to Images. CVPR (2) 2006: 2064-2071 - [c11]Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
Max-margin classification of incomplete data. NIPS 2006: 233-240 - [c10]John C. Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller:
Using Combinatorial Optimization within Max-Product Belief Propagation. NIPS 2006: 369-376 - [c9]Gal Elidan, Ian McGraw, Daphne Koller:
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing. UAI 2006 - 2005
- [j2]Yoseph Barash, Gal Elidan, Tommy Kaplan, Nir Friedman:
CIS: compound importance sampling method for protein-DNA binding site p-value estimation. Bioinform. 21(5): 596-600 (2005) - [j1]Gal Elidan, Nir Friedman:
Learning Hidden Variable Networks: The Information Bottleneck Approach. J. Mach. Learn. Res. 6: 81-127 (2005) - [c8]Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman:
Towards an Integrated Protein-Protein Interaction Network. RECOMB 2005: 14-30 - 2004
- [b1]Gal Elidan:
Learning hidden variables in probabilistic graphical models (למידת משתנים חבויים במודלים גרפיים הסתברותיים.). Hebrew University of Jerusalem, Israel, 2004 - [c7]Iftach Nachman, Gal Elidan, Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Networks. UAI 2004: 400-409 - 2003
- [c6]Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan:
Modeling dependencies in protein-DNA binding sites. RECOMB 2003: 28-37 - [c5]Gal Elidan, Nir Friedman:
The Information Bottleneck EM Algorithm. UAI 2003: 200-208 - 2002
- [c4]Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans:
Data Perturbation for Escaping Local Maxima in Learning. AAAI/IAAI 2002: 132-139 - 2001
- [c3]Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman:
Inferring subnetworks from perturbed expression profiles. ISMB (Supplement of Bioinformatics) 2001: 215-224 - [c2]Gal Elidan, Nir Friedman:
Learning the Dimensionality of Hidden Variables. UAI 2001: 144-151 - 2000
- [c1]Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller:
Discovering Hidden Variables: A Structure-Based Approach. NIPS 2000: 479-485
Coauthor Index
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