| 2026 | AAAI | Unsupervised Feature Selection Through Group Discovery. | Shira Lifshitz, Ofir Lindenbaum, Gal Mishne, Ron Meir, Hadas Benisty |
| 2025 | AAAI | Unsupervised Translation of Emergent Communication. | Ido Levy, Orr Paradise, Boaz Carmeli, Ron Meir, Shafi Goldwasser, Yonatan Belinkov |
| 2025 | ICLR | CtD: Composition through Decomposition in Emergent Communication. | Boaz Carmeli, Ron Meir, Yonatan Belinkov |
| 2024 | ACL | Concept-Best-Matching: Evaluating Compositionality In Emergent Communication. | Boaz Carmeli, Yonatan Belinkov, Ron Meir |
| 2024 | COLT | Statistical curriculum learning: An elimination algorithm achieving an oracle risk. | Omer Cohen, Ron Meir, Nir Weinberger |
| 2024 | ISIT | Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metrics. | Dror Freirich, Nir Weinberger, Ron Meir |
| 2023 | AAAI | Emergent Quantized Communication. | Boaz Carmeli, Ron Meir, Yonatan Belinkov |
| 2022 | AISTATS | Metalearning Linear Bandits by Prior Update. | Amit Peleg, Naama Pearl, Ron Meir |
| 2021 | ICML | Ensemble Bootstrapping for Q-Learning. | Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir |
| 2020 | ICML | Discount Factor as a Regularizer in Reinforcement Learning. | Ron Amit, Ron Meir, Kamil Ciosek |
| 2020 | ICML | Option Discovery in the Absence of Rewards with Manifold Analysis. | Amitay Bar, Ronen Talmon, Ron Meir |
| 2019 | ICML | Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. | Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar |
| 2018 | ICML | Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory. | Ron Amit, Ron Meir |
| 2011 | ICML | Integrating Partial Model Knowledge in Model Free RL Algorithms. | Aviv Tamar, Dotan Di Castro, Ron Meir |
| 2005 | ICML | Reinforcement learning with Gaussian processes. | Yaakov Engel, Shie Mannor, Ron Meir |
| 2004 | COLT | Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers. | Arik Azran, Ron Meir |
| 2003 | COLT | Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. | Ilya Desyatnikov, Ron Meir |
| 2003 | ICML | Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning. | Yaakov Engel, Shie Mannor, Ron Meir |
| 2002 | COLT | The Consistency of Greedy Algorithms for Classification. | Shie Mannor, Ron Meir, Tong Zhang |
| 2001 | COLT | Geometric Bounds for Generalization in Boosting. | Shie Mannor, Ron Meir |
| 2000 | COLT | Localized Boosting. | Ron Meir, Ran El-Yaniv, Shai Ben-David |
| 1999 | IJCNN | Exploiting the virtue of redundancy. | Amir Karniel, Ron Meir, Gideon F. Inbar |
| 1998 | ESANN | Polyhedral mixture of linear experts for many-to-one mapping inversion. | Amir Karniel, Ron Meir, Gideon F. Inbar |
| 1997 | COLT | Performance Bounds for Nonlinear Time Series Prediction. | Ron Meir |
| 1996 | COLT | Towards Robust Model Selection Using Estimation and Approximation Error Bounds. | Joel Ratsaby, Ron Meir, Vitaly Maiorov |
| 1994 | ICPR | Empirical risk minimization versus maximum-likelihood estimation: A case study. | Ron Meir |