Ron Meir - RankMe

121 papers A* 24A 1B 3Journal 67Unranked 26

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CtD: Composition through Decomposition in Emergent Communication. CoRR Boaz Carmeli, Ron Meir, Yonatan Belinkov
2026	J	`jnl`	Investigating the Development of Task-Oriented Communication in Vision-Language Models. CoRR Boaz Carmeli, Orr Paradise, Shafi Goldwasser, Yonatan Belinkov, Ron Meir
2026	J	`jnl`	Optimal L2 Regularization in High-dimensional Continual Linear Regression. CoRR Gilad Karpel, Edward Moroshko, Ran Levinstein, Ron Meir, Daniel Soudry, Itay Evron
2026	A*	`conf`	Unsupervised Feature Selection Through Group Discovery. AAAI Shira Lifshitz, Ofir Lindenbaum, Gal Mishne, Ron Meir, Hadas Benisty
2025	A*	`conf`	CtD: Composition through Decomposition in Emergent Communication. ICLR Boaz Carmeli, Ron Meir, Yonatan Belinkov
2025	—	`conf`	Identifying Internal Control in Biological Systems Through Machine Learning. KST Ron Teichner, Ron Meir
2025	J	`jnl`	Unsupervised Feature Selection Through Group Discovery. CoRR Shira Lifshitz, Ofir Lindenbaum, Gal Mishne, Ron Meir, Hadas Benisty
2025	J	`jnl`	Unsupervised Invariant Risk Minimization. CoRR Yotam Norman, Ron Meir
2025	A*	`conf`	Unsupervised Translation of Emergent Communication. AAAI Ido Levy, Orr Paradise, Boaz Carmeli, Ron Meir, Shafi Goldwasser, Yonatan Belinkov
2025	J	`jnl`	Unsupervised Translation of Emergent Communication. CoRR Ido Levy, Orr Paradise, Boaz Carmeli, Ron Meir, Shafi Goldwasser, Yonatan Belinkov
2024	J	`jnl`	Analysis of the Identifying Regulation With Adversarial Surrogates Algorithm. IEEE Control. Syst. Lett. Ron Teichner, Ron Meir, Michael Margaliot
2024	J	`jnl`	Analysis of the Identifying Regulation with Adversarial Surrogates Algorithm. CoRR Ron Teichner, Ron Meir, Michael Margaliot
2024	B	`conf`	Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metrics. ISIT Dror Freirich, Nir Weinberger, Ron Meir
2024	J	`jnl`	Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metrics. CoRR Dror Freirich, Nir Weinberger, Ron Meir
2024	—	`conf`	Concept-Best-Matching: Evaluating Compositionality In Emergent Communication. ACL (Findings) Boaz Carmeli, Yonatan Belinkov, Ron Meir
2024	J	`jnl`	Concept-Best-Matching: Evaluating Compositionality in Emergent Communication. CoRR Boaz Carmeli, Yonatan Belinkov, Ron Meir
2024	J	`jnl`	Data-dependent and Oracle Bounds on Forgetting in Continual Learning. CoRR Lior Friedman, Ron Meir
2024	A*	`conf`	Statistical curriculum learning: An elimination algorithm achieving an oracle risk. COLT Omer Cohen, Ron Meir, Nir Weinberger
2024	J	`jnl`	Statistical curriculum learning: An elimination algorithm achieving an oracle risk. CoRR Omer Cohen, Ron Meir, Nir Weinberger
2023	—	`conf`	Adaptive Meta-Learning via data-dependent PAC-Bayes bounds. CoLLAs Lior Friedman, Ron Meir
2023	—	`conf`	Discrete-Time Kalman Filter Error Bounds in the Presence of Misspecified Measurements. ECC Ron Teichner, Ron Meir
2023	A*	`conf`	Emergent Quantized Communication. AAAI Boaz Carmeli, Ron Meir, Yonatan Belinkov
2023	J	`jnl`	Identifying Dynamic Regulation with Adversarial Surrogates. CoRR Ron Teichner, Naama Brenner, Ron Meir
2023	A*	`conf`	Meta-Learning Adversarial Bandit Algorithms. NeurIPS Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
2023	J	`jnl`	Meta-Learning Adversarial Bandit Algorithms. CoRR Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
2023	A*	`conf`	Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint. NeurIPS Dror Freirich, Tomer Michaeli, Ron Meir
2023	J	`jnl`	Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint. CoRR Dror Freirich, Tomer Michaeli, Ron Meir
2022	J	`jnl`	Emergent Quantized Communication. CoRR Boaz Carmeli, Ron Meir, Yonatan Belinkov
2022	—	`conf`	Enhancing Causal Estimation through Unlabeled Offline Data. ICFSP Ron Teichner, Danny Eytan, Ron Meir
2022	A*	`conf`	Integral Probability Metrics PAC-Bayes Bounds. NeurIPS Ron Amit, Baruch Epstein, Shay Moran, Ron Meir
2022	J	`jnl`	Integral Probability Metrics PAC-Bayes Bounds. CoRR Ron Amit, Baruch Epstein, Shay Moran, Ron Meir
2022	A	`conf`	Metalearning Linear Bandits by Prior Update. AISTATS Amit Peleg, Naama Pearl, Ron Meir
2022	J	`jnl`	Online Meta-Learning in Adversarial Multi-Armed Bandits. CoRR Ilya Osadchiy, Kfir Y. Levy, Ron Meir
2021	A*	`conf`	A Theory of the Distortion-Perception Tradeoff in Wasserstein Space. NeurIPS Dror Freirich, Tomer Michaeli, Ron Meir
2021	J	`jnl`	A Theory of the Distortion-Perception Tradeoff in Wasserstein Space. CoRR Dror Freirich, Tomer Michaeli, Ron Meir
2021	A*	`conf`	Ensemble Bootstrapping for Q-Learning. ICML Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir
2021	J	`jnl`	Ensemble Bootstrapping for Q-Learning. CoRR Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir
2021	J	`jnl`	Metalearning Linear Bandits by Prior Update. CoRR Amit Peleg, Naama Pearl, Ron Meir
2020	A*	`conf`	Discount Factor as a Regularizer in Reinforcement Learning. ICML Ron Amit, Ron Meir, Kamil Ciosek
2020	J	`jnl`	Discount Factor as a Regularizer in Reinforcement Learning. CoRR Ron Amit, Ron Meir, Kamil Ciosek
2020	J	`jnl`	Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints. Neural Comput. Yuval Harel, Ron Meir
2020	A*	`conf`	Option Discovery in the Absence of Rewards with Manifold Analysis. ICML Amitay Bar, Ronen Talmon, Ron Meir
2020	J	`jnl`	Option Discovery in the Absence of Rewards with Manifold Analysis. CoRR Amitay Bar, Ronen Talmon, Ron Meir
2019	A*	`conf`	Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. ICML Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar
2019	J	`jnl`	Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders. CoRR Baruch Epstein, Ron Meir
2019	J	`jnl`	PAC Guarantees for Concurrent Reinforcement Learning with Restricted Communication. CoRR Or Raveh, Ron Meir
2018	J	`jnl`	Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. CoRR Dror Freirich, Ron Meir, Aviv Tamar
2018	—	`conf`	Joint Autoencoders: A Flexible Meta-learning Framework. ECML/PKDD (1) Baruch Epstein, Ron Meir, Tomer Michaeli
2018	A*	`conf`	Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory. ICML Ron Amit, Ron Meir
2018	J	`jnl`	Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation. Neural Comput. Yuval Harel, Ron Meir, Manfred Opper
2017	J	`jnl`	Integer Forcing: Effective SNR Distribution and Practical Block-Based Schemes. CoRR Ron Meir, Omer Gurewitz, Asaf Cohen
2017	J	`jnl`	Joint auto-encoders: a flexible multi-task learning framework. CoRR Baruch Epstein, Ron Meir, Tomer Michaeli
2017	J	`jnl`	Learning an attention model in an artificial visual system. CoRR Alon Hazan, Yuval Harel, Ron Meir
2017	J	`jnl`	Lifelong Learning by Adjusting Priors. CoRR Ron Amit, Ron Meir
2016	J	`jnl`	Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery. IEEE J. Sel. Top. Signal Process. Gal Mishne, Ronen Talmon, Ron Meir, Jackie Schiller, Maria Lavzin, Uri Dubin, Ronald R. Coifman
2015	—	`conf`	A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. NIPS Yuval Harel, Ron Meir, Manfred Opper
2014	—	`conf`	Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights. NIPS Daniel Soudry, Itay Hubara, Ron Meir
2014	—	`conf`	Optimal Neural Codes for Control and Estimation. NIPS Alex K. Susemihl, Ron Meir, Manfred Opper
2014	J	`jnl`	Optimal Population Codes for Control and Estimation. CoRR Alex K. Susemihl, Ron Meir, Manfred Opper
2014	J	`jnl`	The neuronal response at extended timescales: a linearized spiking input-output relation. Frontiers Comput. Neurosci. Daniel Soudry, Ron Meir
2014	J	`jnl`	The neuronal response at extended timescales: long-term correlations without long-term memory. Frontiers Comput. Neurosci. Daniel Soudry, Ron Meir
2012	J	`jnl`	Conductance-Based Neuron Models and the Slow Dynamics of Excitability. Frontiers Comput. Neurosci. Daniel Soudry, Ron Meir
2012	J	`jnl`	Integrating a Partial Model into Model Free Reinforcement Learning. J. Mach. Learn. Res. Aviv Tamar, Dotan Di Castro, Ron Meir
2011	—	`conf`	Analytical Results for the Error in Filtering of Gaussian Processes. NIPS Alex K. Susemihl, Ron Meir, Manfred Opper
2011	J	`jnl`	Delayed feedback control requires an internal forward model. Biol. Cybern. Dmitry Volkinshtein, Ron Meir
2011	J	`jnl`	Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms CoRR Philip Derbeko, Ran El-Yaniv, Ron Meir
2011	A*	`conf`	Integrating Partial Model Knowledge in Model Free RL Algorithms. ICML Aviv Tamar, Dotan Di Castro, Ron Meir
2010	J	`jnl`	A Convergent Online Single Time Scale Actor Critic Algorithm. J. Mach. Learn. Res. Dotan Di Castro, Ron Meir
2010	J	`jnl`	Error-Based Analysis of Optimal Tuning Functions Explains Phenomena Observed in Sensory Neurons. Frontiers Comput. Neurosci. Steve Yaeli, Ron Meir
2010	J	`jnl`	History-Dependent Dynamics in a Generic Model of Ion Channels - An Analytic Study. Frontiers Comput. Neurosci. Daniel Soudry, Ron Meir
2009	J	`jnl`	A sparsity driven kernel machine based on minimizing a generalization error bound. Pattern Recognit. Dori Peleg, Ron Meir
2009	J	`jnl`	Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration. Neural Comput. Omer Bobrowski, Ron Meir, Yonina C. Eldar
2009	J	`jnl`	Delays and Oscillations in Networks of Spiking Neurons: A Two-Timescale Analysis. Neural Comput. Dotan Di Castro, Ron Meir, Irad Yavneh
2009	J	`jnl`	On the precarious path of reverse neuro-engineering. Frontiers Comput. Neurosci. Shimon Marom, Ron Meir, Erez Braun, Asaf Gal, Einat Kermany, Danny Eytan
2008	J	`jnl`	A bilinear formulation for vector sparsity optimization. Signal Process. Dori Peleg, Ron Meir
2008	J	`jnl`	Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment. PLoS Comput. Biol. Avner Wallach, Danny Eytan, Shimon Marom, Ron Meir
2008	—	`conf`	Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation. NIPS Dotan Di Castro, Dmitry Volkinshtein, Ron Meir
2007	—	`conf`	A neural network implementing optimal state estimation based on dynamic spike train decoding. NIPS Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar
2007	J	`jnl`	Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule. Neural Comput. Dorit Baras, Ron Meir
2007	J	`jnl`	Size-density spectra and their application to image classification. Pattern Recognit. Igor Zingman, Ron Meir, Ran El-Yaniv
2005	A*	`ed.`	Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings COLT Peter Auer, Ron Meir
2005	A*	`conf`	Reinforcement learning with Gaussian processes. ICML Yaakov Engel, Shie Mannor, Ron Meir
2005	J	`jnl`	Semantic-oriented 3d shape retrieval using relevance feedback. Vis. Comput. George Leifman, Ron Meir, Ayellet Tal
2004	—	`conf`	A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound. NIPS Dori Peleg, Ron Meir
2004	A*	`conf`	Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers. COLT Arik Azran, Ron Meir
2004	J	`jnl`	Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. J. Artif. Intell. Res. Philip Derbeko, Ran El-Yaniv, Ron Meir
2004	J	`jnl`	The kernel recursive least-squares algorithm. IEEE Trans. Signal Process. Yaakov Engel, Shie Mannor, Ron Meir
2003	A*	`conf`	Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning. ICML Yaakov Engel, Shie Mannor, Ron Meir
2003	A*	`conf`	Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. COLT Ilya Desyatnikov, Ron Meir
2003	—	`conf`	Error Bounds for Transductive Learning via Compression and Clustering. NIPS Philip Derbeko, Ran El-Yaniv, Ron Meir
2003	J	`jnl`	Generalization Error Bounds for Bayesian Mixture Algorithms. J. Mach. Learn. Res. Ron Meir, Tong Zhang
2003	J	`jnl`	Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity. J. Mach. Learn. Res. Shie Mannor, Ron Meir, Tong Zhang
2003	—	`conf`	Towards Behaviometric Security Systems: Learning to Identify a Typist. PKDD Mordechai Nisenson, Ido Yariv, Ran El-Yaniv, Ron Meir
2002	—	`conf`	An Introduction to Boosting and Leveraging. Machine Learning Summer School Ron Meir, Gunnar Rätsch
2002	—	`conf`	Data-Dependent Bounds for Bayesian Mixture Methods. NIPS Ron Meir, Tong Zhang
2002	J	`jnl`	On the Existence of Linear Weak Learners and Applications to Boosting. Mach. Learn. Shie Mannor, Ron Meir
2002	—	`conf`	Sparse Online Greedy Support Vector Regression. ECML Yaakov Engel, Shie Mannor, Ron Meir
2002	A*	`conf`	The Consistency of Greedy Algorithms for Classification. COLT Shie Mannor, Ron Meir, Tong Zhang
2002	—	`conf`	Variance Optimized Bagging. ECML Philip Derbeko, Ran El-Yaniv, Ron Meir
2001	J	`jnl`	Best estimated inverse versus inverse of the best estimator. Neural Networks Amir Karniel, Ron Meir, Gideon F. Inbar
2001	—	`conf`	Geometric Bounds for Generalization in Boosting. COLT/EuroCOLT Shie Mannor, Ron Meir
2001	J	`jnl`	Lower bounds for multivariate approximation by affine-invariant dictionaries. IEEE Trans. Inf. Theory Vitaly Maiorov, Ron Meir
2001	J	`jnl`	Polyhedral mixture of linear experts for many-to-one mapping inversion and multiple controllers. Neurocomputing Amir Karniel, Ron Meir, Gideon F. Inbar
2000	A*	`conf`	Localized Boosting. COLT Ron Meir, Ran El-Yaniv, Shai Ben-David
2000	J	`jnl`	Nonparametric Time Series Prediction Through Adaptive Model Selection. Mach. Learn. Ron Meir
2000	J	`jnl`	On the near optimality of the stochastic approximation of smooth functions by neural networks. Adv. Comput. Math. Vitaly Maiorov, Ron Meir
2000	J	`jnl`	On the optimality of neural-network approximation using incremental algorithms. IEEE Trans. Neural Networks Learn. Syst. Ron Meir, V. E. Maiorov
2000	—	`conf`	Weak Learners and Improved Rates of Convergence in Boosting. NIPS Shie Mannor, Ron Meir
1999	J	`jnl`	Distortion bounds for vector quantizers with finite codebook size. IEEE Trans. Inf. Theory Ron Meir, Vitaly Maiorov
1999	B	`conf`	Exploiting the virtue of redundancy. IJCNN Amir Karniel, Ron Meir, Gideon F. Inbar
1998	—	`conf`	Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS Peter L. Bartlett, Vitaly Maiorov, Ron Meir
1998	J	`jnl`	Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. Neural Comput. Peter L. Bartlett, Vitaly Maiorov, Ron Meir
1998	J	`jnl`	Error Bounds for Functional Approximation and Estimation Using Mixtures of Experts. IEEE Trans. Inf. Theory Assaf J. Zeevi, Ron Meir, Vitaly Maiorov
1998	—	`conf`	On the Optimality of Incremental Neural Network Algorithms. NIPS Ron Meir, Vitaly Maiorov
1998	B	`conf`	Polyhedral mixture of linear experts for many-to-one mapping inversion. ESANN Amir Karniel, Ron Meir, Gideon F. Inbar
1997	A*	`conf`	Performance Bounds for Nonlinear Time Series Prediction. COLT Ron Meir
1997	—	`conf`	Structural Risk Minimization for Nonparametric Time Series Prediction. NIPS Ron Meir
1996	—	`conf`	Time Series Prediction using Mixtures of Experts. NIPS Assaf J. Zeevi, Ron Meir, Robert J. Adler
1996	A*	`conf`	Towards Robust Model Selection Using Estimation and Approximation Error Bounds. COLT Joel Ratsaby, Ron Meir, Vitaly Maiorov
1994	—	`conf`	Empirical risk minimization versus maximum-likelihood estimation: A case study. ICPR (2) Ron Meir
1990	J	`jnl`	The Effect of Learning on the Evolution of Asexual Populations. Complex Syst. José F. Fontanari, Ron Meir