Shalit, Uri

37 publications

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

AISTATS 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

ICML 2025 Set Valued Predictions for Robust Domain Generalization Ron Tsibulsky, Daniel Nevo, Uri Shalit

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

NeurIPSW 2024 Efficient Experimentation for Estimation of Continuous and Discrete Conditional Treatment Effects Muhammed Razzak, Panagiotis Tigas, Andrew Jesson, Yarin Gal, Uri Shalit

NeurIPS 2024 When to Act and When to Ask: Policy Learning with Deferral Under Hidden Confounding Marah Ghoummaid, Uri Shalit

ICML 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

ICLR 2023 Malign Overfitting: Interpolation and Invariance Are Fundamentally at Odds Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

JMLR 2022 Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag

NeurIPSW 2022 Malign Overfitting: Interpolation and Invariance Are Fundamentally at Odds Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

ICLR 2022 On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, 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 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

UAI 2021 Bandits with Partially Observable Confounded Data Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni

NeurIPS 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, Uri Shalit, Yarin Gal

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

NeurIPSW 2021 Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit

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

ICML 2021 Quantifying Ignorance in Individual-Level Causal-Effect Estimates Under Hidden Confounding Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

NeurIPS 2020 A Causal View of Compositional Zero-Shot Recognition Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

ICLRW 2020 Generative ODE Modeling with Known Unknowns Ori Linial, Uri Shalit

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

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

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

MLHC 2020 Using Deep Networks for Scientific Discovery in Physiological Signals Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit

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

NeurIPS 2018 Removing Hidden Confounding by Experimental Grounding Nathan Kallus, Aahlad Manas Puli, Uri Shalit

NeurIPS 2017 Causal Effect Inference with Deep Latent-Variable Models Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling

ICML 2017 Estimating Individual Treatment Effect: Generalization Bounds and Algorithms Uri Shalit, Fredrik D. Johansson, David Sontag

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

ICML 2016 Learning Representations for Counterfactual Inference Fredrik Johansson, Uri Shalit, David Sontag

ICML 2014 Coordinate-Descent for Learning Orthogonal Matrices Through Givens Rotations Uri Shalit, Gal Chechik

ICML 2013 Modeling Musical Influence with Topic Models Uri Shalit, Daphna Weinshall, Gal Chechik

JMLR 2012 Online Learning in the Embedded Manifold of Low-Rank Matrices Uri Shalit, Daphna Weinshall, Gal Chechik

JMLR 2010 Large Scale Online Learning of Image Similarity Through Ranking Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio

NeurIPS 2010 Online Learning in the Manifold of Low-Rank Matrices Uri Shalit, Daphna Weinshall, Gal Chechik

NeurIPS 2009 An Online Algorithm for Large Scale Image Similarity Learning Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio