An Online Algorithm for Large Scale Image Similarity Learning. - Gal Chechik; Uri Shalit; Varun Sharma; Samy Bengio (NIPS, 2009)

Online Learning in The Manifold of Low-Rank Matrices. - Uri Shalit; Daphna Weinshall; Gal Chechik (NIPS, 2010)

Large Scale Online Learning of Image Similarity Through Ranking. - Gal Chechik; Varun Sharma; Uri Shalit; Samy Bengio (J. Mach. Learn. Res., 2010)

Online Learning in the Embedded Manifold of Low-rank Matrices. - Uri Shalit; Daphna Weinshall; Gal Chechik (J. Mach. Learn. Res., 2012)

Modeling Musical Influence with Topic Models. - Uri Shalit; Daphna Weinshall; Gal Chechik (ICML (2), 2013)

FuncISH: learning a functional representation of neural ISH images. - Noa Liscovitch; Uri Shalit; Gal Chechik (Bioinform., 2013)

Efficient coordinate-descent for orthogonal matrices through Givens rotations. - Uri Shalit; Gal Chechik (CoRR, 2013)

Coordinate-descent for learning orthogonal matrices through Givens rotations. - Uri Shalit; Gal Chechik (ICML, 2014)

Learning Sparse Metrics, One Feature at a Time. - Yuval Atzmon; Uri Shalit; Gal Chechik (FE@NIPS, 2015)

Deep Kalman Filters. - Rahul G. Krishnan; Uri Shalit; David A. Sontag (CoRR, 2015)

Scalable streaming learning of dyadic relationships (שער נוסף בעברית: למידת יחסים דיאדיים ממידע זורם.). - Uri Shalit (2015)

Learning Representations for Counterfactual Inference. - Fredrik D. Johansson; Uri Shalit; David A. Sontag (ICML, 2016)

Bounding and Minimizing Counterfactual Error. - Uri Shalit; Fredrik D. Johansson; David A. Sontag (CoRR, 2016)

Structured Inference Networks for Nonlinear State Space Models. - Rahul G. Krishnan; Uri Shalit; David A. Sontag (AAAI, 2017)

Estimating individual treatment effect: generalization bounds and algorithms. - Uri Shalit; Fredrik D. Johansson; David A. Sontag (ICML, 2017)

Causal Effect Inference with Deep Latent-Variable Models. - Christos Louizos; Uri Shalit; Joris M. Mooij; David A. Sontag; Richard S. Zemel; Max Welling (NIPS, 2017)

Removing Hidden Confounding by Experimental Grounding. - Nathan Kallus; Aahlad Manas Puli; Uri Shalit (NeurIPS, 2018)

Building Causal Graphs from Medical Literature and Electronic Medical Records. - Galia Nordon; Gideon Koren; Varda Shalev; Benny Kimelfeld; Uri Shalit; Kira Radinsky (AAAI, 2019)

Explaining Classifiers with Causal Concept Effect (CaCE). - Yash Goyal; Uri Shalit; Been Kim (CoRR, 2019)

Off-Policy Evaluation in Partially Observable Environments. - Guy Tennenholtz; Uri Shalit; Shie Mannor (AAAI, 2020)

Robust Learning with the Hilbert-Schmidt Independence Criterion. - Daniel Greenfeld; Uri Shalit (ICML, 2020)

Using deep networks for scientific discovery in physiological signals. - Tom Beer; Bar Eini-Porat; Sebastian Goodfellow; Danny Eytan; Uri Shalit (MLHC, 2020)

A causal view of compositional zero-shot recognition. - Yuval Atzmon; Felix Kreuk; Uri Shalit; Gal Chechik (NeurIPS, 2020)

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. - Andrew Jesson; Sören Mindermann; Uri Shalit; Yarin Gal (NeurIPS, 2020)

Generative ODE Modeling with Known Unknowns. - Ori Linial; Danny Eytan; Uri Shalit (CoRR, 2020)

Bandits with Partially Observable Offline Data. - Guy Tennenholtz; Uri Shalit; Shie Mannor; Yonathan Efroni (CoRR, 2020)

Generative ODE modeling with known unknowns. - Ori Linial; Neta Ravid; Danny Eytan; Uri Shalit (CHIL, 2021)

Corporate Social Responsibility via Multi-Armed Bandits. - Tom Ron; Omer Ben-Porat; Uri Shalit (FAccT, 2021)

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. - Andrew Jesson; Sören Mindermann; Yarin Gal; Uri Shalit (ICML, 2021)

Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. - Junhyung Park; Uri Shalit; Bernhard Schölkopf; Krikamol Muandet (ICML, 2021)

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. - Andrew Jesson; Panagiotis Tigas; Joost van Amersfoort; Andreas Kirsch 002; Uri Shalit; Yarin Gal (NeurIPS, 2021)

On Calibration and Out-of-Domain Generalization. - Yoav Wald; Amir Feder; Daniel Greenfeld; Uri Shalit (NeurIPS, 2021)

Bandits with partially observable confounded data. - Guy Tennenholtz; Uri Shalit; Shie Mannor; Yonathan Efroni (UAI, 2021)

CausaLM: Causal Model Explanation Through Counterfactual Language Models. - Amir Feder; Nadav Oved; Uri Shalit; Roi Reichart (Comput. Linguistics, 2021)

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study. - Michael Roimi; Rom Gutman; Jonathan Somer; Asaf Ben Arie; Ido Calman; Yaron Bar-Lavie; Udi Gelbshtein; Sigal Liverant-Taub; Arnona Ziv; Danny Eytan; Malka Gorfine; Uri Shalit (J. Am. Medical Informatics Assoc., 2021)

On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. - Guy Tennenholtz; Assaf Hallak; Gal Dalal; Shie Mannor; Gal Chechik; Uri Shalit (ICLR, 2022)

Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. - Andrew Jesson; Alyson Douglas; Peter Manshausen; Maëlys Solal; Nicolai Meinshausen; Philip Stier; Yarin Gal; Uri Shalit (NeurIPS, 2022)

Reinforcement Learning with a Terminator. - Guy Tennenholtz; Nadav Merlis; Lior Shani; Shie Mannor; Uri Shalit; Gal Chechik; Assaf Hallak; Gal Dalal (NeurIPS, 2022)

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. - Andrew Jesson; Alyson Douglas; Peter Manshausen; Nicolai Meinshausen; Philip Stier; Yarin Gal; Uri Shalit (CoRR, 2022)

Malign Overfitting: Interpolation Can Provably Preclude Invariance. - Yoav Wald; Gal Yona; Uri Shalit; Yair Carmon (CoRR, 2022)

Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. - Bar Eini-Porat; Ofra Amir; Danny Eytan; Uri Shalit (J. Biomed. Informatics, 2022)

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. - Fredrik D. Johansson; Uri Shalit; Nathan Kallus; David A. Sontag (J. Mach. Learn. Res., 2022)

Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. - Yoav Wald; Gal Yona; Uri Shalit; Yair Carmon (ICLR, 2023)

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. - Miruna Oprescu; Jacob Dorn; Marah Ghoummaid; Andrew Jesson; Nathan Kallus; Uri Shalit (ICML, 2023)

iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. - Neta Ravid Tannenbaum; Omer Gottesman; Azadeh Assadi; Mjaye Mazwi; Uri Shalit; Danny Eytan (PLoS Comput. Biol., 2023)

When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding. - Marah Ghoummaid; Uri Shalit (NeurIPS, 2024)

Aiming for Relevance. - Bar Eini-Porat; Danny Eytan; Uri Shalit (CoRR, 2024)

Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators. - Ori Linial; Guy Tennenholtz; Uri Shalit (CoRR, 2024)

On the ERM Principle in Meta-Learning. - Yannay Alon; Steve Hanneke; Shay Moran; Uri Shalit (CoRR, 2024)

BIG-Bench Extra Hard. - Mehran Kazemi; Bahare Fatemi; Hritik Bansal; John Palowitch; Chrysovalantis Anastasiou; Sanket Vaibhav Mehta; Lalit K. Jain; Virginia Aglietti; Disha Jindal; Peter Chen; Nishanth Dikkala; Gladys Tyen; et al. (ACL (1), 2025)

Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition. - Jake Fawkes; Lucile Ter-Minassian; Desi R. Ivanova; Uri Shalit; Christopher C. Holmes (AISTATS, 2025)

Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions. - Omer Noy Klein; Alihan Hüyük; Ron Shamir; Uri Shalit; Mihaela van der Schaar (AISTATS, 2025)

Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees. - Tomer Meir; Uri Shalit; Malka Gorfine (CoRR, 2025)

BIG-Bench Extra Hard. - Mehran Kazemi; Bahare Fatemi; Hritik Bansal; John Palowitch; Chrysovalantis Anastasiou; Sanket Vaibhav Mehta; Kshitij Jain 001; Virginia Aglietti; Disha Jindal; Peter Chen; Nishanth Dikkala; Gladys Tyen; et al. (CoRR, 2025)

Set Valued Predictions For Robust Domain Generalization. - Ron Tsibulsky; Daniel Nevo; Uri Shalit (CoRR, 2025)

From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies. - Rom Gutman; Shimon Sheiba; Omer Noy Klein; Naama Dekel Bird; Amit Gruber; Doron Aronson; Oren Caspi; Uri Shalit (CoRR, 2025)