| 2025 | ACL | 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, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V. Le, Orhan Firat |
| 2025 | AISTATS | 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 |
| 2025 | AISTATS | Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions. | Omer Noy Klein, Alihan Hyk, Ron Shamir, Uri Shalit, Mihaela van der Schaar |
| 2025 | ICML | Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees. | Tomer Meir, Uri Shalit, Malka Gorfine |
| 2025 | ICML | Set Valued Predictions For Robust Domain Generalization. | Ron Tsibulsky, Daniel Nevo, Uri Shalit |
| 2023 | ICLR | Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. | Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon |
| 2023 | ICML | B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. | Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit |
| 2022 | ICLR | On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. | Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit |
| 2021 | ICML | Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. | Andrew Jesson, Sren Mindermann, Yarin Gal, Uri Shalit |
| 2021 | ICML | Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. | Junhyung Park, Uri Shalit, Bernhard Schlkopf, Krikamol Muandet |
| 2021 | UAI | Bandits with partially observable confounded data. | Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni |
| 2020 | AAAI | Off-Policy Evaluation in Partially Observable Environments. | Guy Tennenholtz, Uri Shalit, Shie Mannor |
| 2020 | ICML | Robust Learning with the Hilbert-Schmidt Independence Criterion. | Daniel Greenfeld, Uri Shalit |
| 2019 | AAAI | Building Causal Graphs from Medical Literature and Electronic Medical Records. | Galia Nordon, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, Kira Radinsky |
| 2017 | AAAI | Structured Inference Networks for Nonlinear State Space Models. | Rahul G. Krishnan, Uri Shalit, David A. Sontag |
| 2017 | ICML | Estimating individual treatment effect: generalization bounds and algorithms. | Uri Shalit, Fredrik D. Johansson, David A. Sontag |
| 2016 | ICML | Learning Representations for Counterfactual Inference. | Fredrik D. Johansson, Uri Shalit, David A. Sontag |
| 2014 | ICML | Coordinate-descent for learning orthogonal matrices through Givens rotations. | Uri Shalit, Gal Chechik |
| 2013 | ICML | Modeling Musical Influence with Topic Models. | Uri Shalit, Daphna Weinshall, Gal Chechik |
| 2009 | IBPRIA | Large Scale Online Learning of Image Similarity through Ranking. | Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio |