Learning by Choice of Internal Representations. - Tal Grossman; Ronny Meir; Eytan Domany (Complex Syst., 1988)

Relaxation Networks for Large Supervised Learning Problems. - Joshua Alspector; Robert B. Allen; Anthony Jayakumar; Torsten Zeppenfeld; Ronny Meir (NIPS, 1990)

The Effect of Learning on the Evolution of Asexual Populations. - José F. Fontanari; Ron Meir (Complex Syst., 1990)

Computing with Arrays of Coupled Oscillators: An Application to Preattentive Texture Discrimination. - Pierre Baldi; Ronny Meir (Neural Comput., 1990)

On Deriving Deterministic Learning Rules from Stochastic Systems. - Ronny Meir (Int. J. Neural Syst., 1991)

On Learning Noisy Threshold Functions with Finite Precision Weights. - Ronny Meir; José F. Fontanari (COLT, 1992)

A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks. - Joshua Alspector; Ronny Meir; Ben P. Yuhas; Anthony Jayakumar; D. Lippe (NIPS, 1992)

Bias, Variance and the Combination of Least Squares Estimators. - Ronny Meir (NIPS, 1994)

On the Stochastic Complexity of Learning Realizable and Unrealizable Rules. - Ronny Meir; Neri Merhav (Mach. Learn., 1995)

Empirical Risk Minimization versus Maximum-Likelihood Estimation: A Case Study. - Ronny Meir (Neural Comput., 1995)

Towards Robust Model Selection Using Estimation and Approximation Error Bounds. - Joel Ratsaby; Ron Meir; Vitaly Maiorov (COLT, 1996)

Time Series Prediction using Mixtures of Experts. - Assaf J. Zeevi; Ron Meir; Robert J. Adler (NIPS, 1996)

Performance Bounds for Nonlinear Time Series Prediction. - Ron Meir (COLT, 1997)

Structural Risk Minimization for Nonparametric Time Series Prediction. - Ron Meir (NIPS, 1997)

Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds. - Assaf J. Zeevi; Ronny Meir (Neural Networks, 1997)

Polyhedral mixture of linear experts for many-to-one mapping inversion. - Amir Karniel; Ron Meir; Gideon F. Inbar (ESANN, 1998)

On the Optimality of Incremental Neural Network Algorithms. - Ron Meir; Vitaly Maiorov (NIPS, 1998)

Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. - Peter L. Bartlett; Vitaly Maiorov; Ron Meir (Neural Comput., 1998)

Error Bounds for Functional Approximation and Estimation Using Mixtures of Experts. - Assaf J. Zeevi; Ron Meir; Vitaly Maiorov (IEEE Trans. Inf. Theory, 1998)

Approximation bounds for smooth functions in C(Rd) by neural and mixture networks. - V. E. Maiorov; R. S. Meir (IEEE Trans. Neural Networks, 1998)

Exploiting the virtue of redundancy. - Amir Karniel; Ron Meir; Gideon F. Inbar (IJCNN, 1999)

Distortion bounds for vector quantizers with finite codebook size. - Ron Meir; Vitaly Maiorov (IEEE Trans. Inf. Theory, 1999)

Localized Boosting. - Ron Meir; Ran El-Yaniv; Shai Ben-David (COLT, 2000)

Weak Learners and Improved Rates of Convergence in Boosting. - Shie Mannor; Ron Meir (NIPS, 2000)

On the near optimality of the stochastic approximation of smooth functions by neural networks. - Vitaly Maiorov; Ron Meir (Adv. Comput. Math., 2000)

Nonparametric Time Series Prediction Through Adaptive Model Selection. - Ron Meir (Mach. Learn., 2000)

On the optimality of neural-network approximation using incremental algorithms. - Ron Meir; V. E. Maiorov (IEEE Trans. Neural Networks Learn. Syst., 2000)

Geometric Bounds for Generalization in Boosting. - Shie Mannor; Ron Meir (COLT/EuroCOLT, 2001)

Polyhedral mixture of linear experts for many-to-one mapping inversion and multiple controllers. - Amir Karniel; Ron Meir; Gideon F. Inbar (Neurocomputing, 2001)

Best estimated inverse versus inverse of the best estimator. - Amir Karniel; Ron Meir; Gideon F. Inbar (Neural Networks, 2001)

Lower bounds for multivariate approximation by affine-invariant dictionaries. - Vitaly Maiorov; Ron Meir (IEEE Trans. Inf. Theory, 2001)

The Consistency of Greedy Algorithms for Classification. - Shie Mannor; Ron Meir; Tong Zhang 001 (COLT, 2002)

Variance Optimized Bagging. - Philip Derbeko; Ran El-Yaniv; Ron Meir (ECML, 2002)

Sparse Online Greedy Support Vector Regression. - Yaakov Engel; Shie Mannor; Ron Meir (ECML, 2002)

An Introduction to Boosting and Leveraging. - Ron Meir; Gunnar Rätsch (Machine Learning Summer School, 2002)

Data-Dependent Bounds for Bayesian Mixture Methods. - Ron Meir; Tong Zhang 001 (NIPS, 2002)

On the Existence of Linear Weak Learners and Applications to Boosting. - Shie Mannor; Ron Meir (Mach. Learn., 2002)

Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. - Ilya Desyatnikov; Ron Meir (COLT, 2003)

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

Error Bounds for Transductive Learning via Compression and Clustering. - Philip Derbeko; Ran El-Yaniv; Ron Meir (NIPS, 2003)

Towards Behaviometric Security Systems: Learning to Identify a Typist. - Mordechai Nisenson; Ido Yariv; Ran El-Yaniv; Ron Meir (PKDD, 2003)

Greedy Algorithms for Classification – Consistency, Convergence Rates, and Adaptivity. - Shie Mannor; Ron Meir; Tong Zhang 001 (J. Mach. Learn. Res., 2003)

Generalization Error Bounds for Bayesian Mixture Algorithms. - Ron Meir; Tong Zhang 001 (J. Mach. Learn. Res., 2003)

Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers. - Arik Azran; Ron Meir (COLT, 2004)

A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound. - Dori Peleg; Ron Meir (NIPS, 2004)

Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. - Philip Derbeko; Ran El-Yaniv; Ron Meir (J. Artif. Intell. Res., 2004)

The kernel recursive least-squares algorithm. - Yaakov Engel; Shie Mannor; Ron Meir (IEEE Trans. Signal Process., 2004)

Reinforcement learning with Gaussian processes. - Yaakov Engel; Shie Mannor; Ron Meir (ICML, 2005)

Semantic-oriented 3d shape retrieval using relevance feedback. - George Leifman; Ron Meir; Ayellet Tal (Vis. Comput., 2005)

A neural network implementing optimal state estimation based on dynamic spike train decoding. - Omer Bobrowski; Ron Meir; Shy Shoham; Yonina C. Eldar (NIPS, 2007)

Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule. - Dorit Baras; Ron Meir (Neural Comput., 2007)

Size-density spectra and their application to image classification. - Igor Zingman; Ron Meir; Ran El-Yaniv (Pattern Recognit., 2007)

Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation. - Dotan Di Castro; Dmitry Volkinshtein; Ron Meir (NIPS, 2008)

Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment. - Avner Wallach; Danny Eytan; Shimon Marom; Ron Meir (PLoS Comput. Biol., 2008)

A bilinear formulation for vector sparsity optimization. - Dori Peleg; Ron Meir (Signal Process., 2008)

On the precarious path of reverse neuro-engineering. - Shimon Marom; Ron Meir; Erez Braun; Asaf Gal; Einat Kermany; Danny Eytan (Frontiers Comput. Neurosci., 2009)

Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration. - Omer Bobrowski; Ron Meir; Yonina C. Eldar (Neural Comput., 2009)

Delays and Oscillations in Networks of Spiking Neurons: A Two-Timescale Analysis. - Dotan Di Castro; Ron Meir; Irad Yavneh (Neural Comput., 2009)

A sparsity driven kernel machine based on minimizing a generalization error bound. - Dori Peleg; Ron Meir (Pattern Recognit., 2009)

History-Dependent Dynamics in a Generic Model of Ion Channels - An Analytic Study. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2010)

Error-Based Analysis of Optimal Tuning Functions Explains Phenomena Observed in Sensory Neurons. - Steve Yaeli; Ron Meir (Frontiers Comput. Neurosci., 2010)

A Convergent Online Single Time Scale Actor Critic Algorithm. - Dotan Di Castro; Ron Meir (J. Mach. Learn. Res., 2010)

Integrating Partial Model Knowledge in Model Free RL Algorithms. - Aviv Tamar; Dotan Di Castro; Ron Meir (ICML, 2011)

Analytical Results for the Error in Filtering of Gaussian Processes. - Alex K. Susemihl; Ron Meir; Manfred Opper (NIPS, 2011)

Delayed feedback control requires an internal forward model. - Dmitry Volkinshtein; Ron Meir (Biol. Cybern., 2011)

Conductance-Based Neuron Models and the Slow Dynamics of Excitability. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2012)

Integrating a Partial Model into Model Free Reinforcement Learning. - Aviv Tamar; Dotan Di Castro; Ron Meir (J. Mach. Learn. Res., 2012)

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

Optimal Neural Codes for Control and Estimation. - Alex K. Susemihl; Ron Meir; Manfred Opper (NIPS, 2014)

Optimal Population Codes for Control and Estimation. - Alex K. Susemihl; Ron Meir; Manfred Opper (CoRR, 2014)

The neuronal response at extended timescales: long-term correlations without long-term memory. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2014)

The neuronal response at extended timescales: a linearized spiking input-output relation. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2014)

A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. - Yuval Harel; Ron Meir; Manfred Opper (NIPS, 2015)

Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery. - Gal Mishne; Ronen Talmon; Ron Meir; Jackie Schiller; Maria Lavzin; Uri Dubin; Ronald R. Coifman (IEEE J. Sel. Top. Signal Process., 2016)

Learning an attention model in an artificial visual system. - Alon Hazan; Yuval Harel; Ron Meir (CoRR, 2017)

Joint auto-encoders: a flexible multi-task learning framework. - Baruch Epstein; Ron Meir; Tomer Michaeli (CoRR, 2017)

Integer Forcing: Effective SNR Distribution and Practical Block-Based Schemes. - Ron Meir; Omer Gurewitz; Asaf Cohen 001 (CoRR, 2017)

Lifelong Learning by Adjusting Priors. - Ron Amit; Ron Meir (CoRR, 2017)

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

Joint Autoencoders: A Flexible Meta-learning Framework. - Baruch Epstein; Ron Meir; Tomer Michaeli (ECML/PKDD (1), 2018)

Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. - Dror Freirich; Ron Meir; Aviv Tamar (CoRR, 2018)

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation. - Yuval Harel; Ron Meir; Manfred Opper (Neural Comput., 2018)

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

Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders. - Baruch Epstein; Ron Meir (CoRR, 2019)

PAC Guarantees for Concurrent Reinforcement Learning with Restricted Communication. - Or Raveh; Ron Meir (CoRR, 2019)

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, 2020)

Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints. - Yuval Harel; Ron Meir (Neural Comput., 2020)

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

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

Metalearning Linear Bandits by Prior Update. - Amit Peleg; Naama Pearl; Ron Meir (AISTATS, 2022)

Enhancing Causal Estimation through Unlabeled Offline Data. - Ron Teichner; Danny Eytan; Ron Meir (ICFSP, 2022)

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

Online Meta-Learning in Adversarial Multi-Armed Bandits. - Ilya Osadchiy; Kfir Y. Levy; Ron Meir (CoRR, 2022)

Emergent Quantized Communication. - Boaz Carmeli; Ron Meir; Yonatan Belinkov (AAAI, 2023)

Adaptive Meta-Learning via data-dependent PAC-Bayes bounds. - Lior Friedman; Ron Meir (CoLLAs, 2023)

Discrete-Time Kalman Filter Error Bounds in the Presence of Misspecified Measurements. - Ron Teichner; Ron Meir (ECC, 2023)

Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint. - Dror Freirich; Tomer Michaeli; Ron Meir (NeurIPS, 2023)

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

Meta-Learning Adversarial Bandit Algorithms. - Mikhail Khodak; Ilya Osadchiy; Keegan Harris; Maria-Florina Balcan; Kfir Y. Levy; Ron Meir; Zhiwei Steven Wu (CoRR, 2023)

Identifying Dynamic Regulation with Adversarial Surrogates. - Ron Teichner; Naama Brenner; Ron Meir (CoRR, 2023)

Concept-Best-Matching: Evaluating Compositionality In Emergent Communication. - Boaz Carmeli; Yonatan Belinkov; Ron Meir (ACL (Findings), 2024)

Statistical curriculum learning: An elimination algorithm achieving an oracle risk. - Omer Cohen; Ron Meir; Nir Weinberger (COLT, 2024)

Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metrics. - Dror Freirich; Nir Weinberger; Ron Meir (ISIT, 2024)

Data-dependent and Oracle Bounds on Forgetting in Continual Learning. - Lior Friedman; Ron Meir (CoRR, 2024)

Analysis of the Identifying Regulation With Adversarial Surrogates Algorithm. - Ron Teichner; Ron Meir; Michael Margaliot (IEEE Control. Syst. Lett., 2024)

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

CtD: Composition through Decomposition in Emergent Communication. - Boaz Carmeli; Ron Meir; Yonatan Belinkov (ICLR, 2025)

Identifying Internal Control in Biological Systems Through Machine Learning. - Ron Teichner; Ron Meir (KST, 2025)

Unsupervised Invariant Risk Minimization. - Yotam Norman; Ron Meir (CoRR, 2025)