Meir, Ron

52 publications

ICLR 2025 CtD: Composition Through Decomposition in Emergent Communication Boaz Carmeli, Ron Meir, Yonatan Belinkov

AAAI 2025 Unsupervised Translation of Emergent Communication Ido Levy, Orr Paradise, Boaz Carmeli, Ron Meir, Shafi Goldwasser, Yonatan Belinkov

COLT 2024 Statistical Curriculum Learning: An Elimination Algorithm Achieving an Oracle Risk Omer Cohen, Ron Meir, Nir Weinberger

CoLLAs 2023 Adaptive Meta-Learning via Data-Dependent PAC-Bayes Bounds Lior Friedman, Ron Meir

AAAI 2023 Emergent Quantized Communication Boaz Carmeli, Ron Meir, Yonatan Belinkov

NeurIPS 2023 Meta-Learning Adversarial Bandit Algorithms Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina F Balcan, Kfir Y. Levy, Ron Meir, Steven Z. Wu

NeurIPS 2023 Perceptual Kalman Filters: Online State Estimation Under a Perfect Perceptual-Quality Constraint Dror Freirich, Tomer Michaeli, Ron Meir

NeurIPSW 2023 The Distortion-Perception Tradeoff in Finite Channels with Arbitrary Distortion Measures Dror Freirich, Nir Weinberger, Ron Meir

AISTATS 2022 Metalearning Linear Bandits by Prior Update Amit Peleg, Naama Pearl, Ron Meir

NeurIPS 2022 Integral Probability Metrics PAC-Bayes Bounds Ron Amit, Baruch Epstein, Shay Moran, Ron Meir

NeurIPS 2021 A Theory of the Distortion-Perception Tradeoff in Wasserstein Space Dror Freirich, Tomer Michaeli, Ron Meir

ICML 2021 Ensemble Bootstrapping for Q-Learning Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir

ICML 2020 Discount Factor as a Regularizer in Reinforcement Learning Ron Amit, Ron Meir, Kamil Ciosek

ICML 2020 Option Discovery in the Absence of Rewards with Manifold Analysis Amitay Bar, Ronen Talmon, Ron Meir

ICML 2019 Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar

ECML-PKDD 2018 Joint Autoencoders: A Flexible Meta-Learning Framework Baruch Epstein, Ron Meir, Tomer Michaeli

ICML 2018 Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory Ron Amit, Ron Meir

NeurIPS 2015 A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Yuval Harel, Ron Meir, Manfred Opper

NeurIPS 2014 Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights Daniel Soudry, Itay Hubara, Ron Meir

NeurIPS 2014 Optimal Neural Codes for Control and Estimation Alex K. Susemihl, Ron Meir, Manfred Opper

JMLR 2012 Integrating a Partial Model into Model Free Reinforcement Learning Aviv Tamar, Dotan Di Castro, Ron Meir

NeurIPS 2011 Analytical Results for the Error in Filtering of Gaussian Processes Alex K. Susemihl, Ron Meir, Manfred Opper

ICML 2011 Integrating Partial Model Knowledge in Model Free RL Algorithms Aviv Tamar, Dotan Di Castro, Ron Meir

JMLR 2010 A Convergent Online Single Time Scale Actor Critic Algorithm Dotan Di Castro, Ron Meir

NeurIPS 2008 Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation Dotan D. Castro, Dmitry Volkinshtein, Ron Meir

NeurIPS 2007 A Neural Network Implementing Optimal State Estimation Based on Dynamic Spike Train Decoding Omer Bobrowski, Ron Meir, Shy Shoham, Yonina Eldar

COLT 2005 Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings Peter Auer, Ron Meir

ICML 2005 Reinforcement Learning with Gaussian Processes Yaakov Engel, Shie Mannor, Ron Meir

NeurIPS 2004 A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound Dori Peleg, Ron Meir

COLT 2004 Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers Arik Azran, Ron Meir

JAIR 2004 Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms Philip Derbeko, Ran El-Yaniv, Ron Meir

ICML 2003 Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning Yaakov Engel, Shie Mannor, Ron Meir

COLT 2003 Data-Dependent Bounds for Multi-Category Classification Based on Convex Losses Ilya Desyatnikov, Ron Meir

NeurIPS 2003 Error Bounds for Transductive Learning via Compression and Clustering Philip Derbeko, Ran El-Yaniv, Ron Meir

JMLR 2003 Generalization Error Bounds for Bayesian Mixture Algorithms Ron Meir, Tong Zhang

JMLR 2003 Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity Shie Mannor, Ron Meir, Tong Zhang

NeurIPS 2002 Data-Dependent Bounds for Bayesian Mixture Methods Ron Meir, Tong Zhang

MLJ 2002 On the Existence of Linear Weak Learners and Applications to Boosting Shie Mannor, Ron Meir

ECML-PKDD 2002 Sparse Online Greedy Support Vector Regression Yaakov Engel, Shie Mannor, Ron Meir

COLT 2002 The Consistency of Greedy Algorithms for Classification Shie Mannor, Ron Meir, Tong Zhang

ECML-PKDD 2002 Variance Optimized Bagging Philip Derbeko, Ran El-Yaniv, Ron Meir

COLT 2001 Geometric Bounds for Generalization in Boosting Shie Mannor, Ron Meir

COLT 2000 Localized Boosting Ron Meir, Ran El-Yaniv, Shai Ben-David

MLJ 2000 Nonparametric Time Series Prediction Through Adaptive Model Selection Ron Meir

NeurIPS 2000 Weak Learners and Improved Rates of Convergence in Boosting Shie Mannor, Ron Meir

NeurIPS 1998 Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks Peter L. Bartlett, Vitaly Maiorov, Ron Meir

NeCo 1998 Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks Peter L. Bartlett, Vitaly Maiorov, Ron Meir

NeurIPS 1998 On the Optimality of Incremental Neural Network Algorithms Ron Meir, Vitaly Maiorov

COLT 1997 Performance Bounds for Nonlinear Time Series Prediction Ron Meir

NeurIPS 1997 Structural Risk Minimization for Nonparametric Time Series Prediction Ron Meir

NeurIPS 1996 Time Series Prediction Using Mixtures of Experts Assaf J. Zeevi, Ron Meir, Robert J. Adler

COLT 1996 Towards Robust Model Selection Using Estimation and Approximation Error Bounds Joel Ratsaby, Ron Meir, Vitaly Maiorov