Publications

Selected recent publications (All publications in Google Scholar)

• Metalearning Linear Bandits by Prior Update, Amit Peleg, Naama Pearl, Ron Meir, AISTATS 2022
• 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.
• Discount Factor as a Regularizer in Reinforcement Learning, R. Amit, R. Meir and K. Ciosek, ICML 2020.
• Option Discovery in the Absence of Rewards with Manifold Analysis, A. Bar, R. Meir and R. Talmon, ICML 2020.
• Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints, Yuval Harel and Ron Meir, Neural Computation 2020
• PAC Guarantees for Concurrent Reinforcement Learning with Restricted Communication, Or Raveh, Ron Meir, arXiv:1905.09951
• Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders, Baruch Epstein, Ron Meir arXiv:1902.01449, 2019
• Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN, Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar, ICML 2019, arXiv:1808.01960
• Lifelong learning by adjusting priors, Ron Amit and Ron Meir, arXiv:1711.01244, ICML 2018
• Joint auto-encoders: a flexible multi-task learning framework, Baruch Epstein, Ron Meir, Tomer Michaeli, ECML 2017, arXiv:1705.10494
• Learning an attention model in an artificial visual system, A. Hazan, Y. Harel, R. Meir, Science of Electrical Engineering (ICSEE), IEEE International Conference, 2016, (arXiv)
• Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discovery , G. Mishne, R. Talmon, R. Meir, J. Schiller, U. Dubin, R. R. Coifman, IEEE J of Selected Topics in Signal Processing, 10(7), 2016 (arXiv)
• Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis, Y. Dordek, D. Soudry, R. Meir, D. Derdikman, eLife, 5, e10094, 2016
• A Tractable approximation to optimal point process filtering: application to neural encoding, Y. Harel, R. Meir, M. Opper, Advances in Neural Information Processing 28 (NIPS 2015)
• Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights, D. Soudry, I. Hubara, R. Meir , Advances in Neural Information Processing 27 (NIPS 2014)
• Optimal Neural Codes for Control and Estimation, A. Susemihl, R. Meir, M. Opper, Advances in Neural Information Processing 27 (NIPS 2014)
• The neuronal response at extended timescales: a linearized spiking input-output relation, D. Soudry and R. Meir, Front. Comput. Neurosci., 8(29), 2014
• The neuronal response at extended timescales: long term correlations without long memory, D. Soudry and R. Meir, Front. Comput. Neurosci., 8(35), 2014
• Dynamic State Estimation Based on Poisson Spike Trains: Towards a Theory of Optimal Encoding Processes,  A. Susemihl, R. Meir and M. Opper, Journal of Statistical Mechanics 3, P03009, 2013 (arxiv version, NIPS version)
• Conductance-based neuron models and the slow dynamics of excitability, D. Soudry and R. Meir, Frontiers in Computational Neuroscience, volume 6:4, 2012.
• Inegrating a Partial Model into Model Free Reinforcement Learning,  Aviv Tamar,  Dotan Di Castro and R. Meir, Journal of Machine Learning Research 13: 1927-1966, 2012 (ICML version)
• Delayed Feedback Control Requires an Internal Forward Model, D. Volkinshtein and R. Meir, Biological Cybernetics 195(1): 41-53, 2011
• Error-based analysis of optimal tuning functions predicts phenomena observed in sensory neurons,  S. Yaeli and R. Meir, Frontiers in Computational Neuroscience, 4:130, 2010
• History dependent dynamics in a generic model of ion channels – an analytic study D. Soudry and R. Meir, Frontiers in Computational Neuroscience, Volume 4:3, 2010
• A Convergent Online Single Time Scale Actor Critic Algorithm, D. Di Castro and R. Meir, Journal of Machine Learning Research, 11: 367-410, 2010
• On the Precarious Path of Reverse Neuro-Engineering,  S. Marom S, R. Meir, E.N. Braun, A.N. Gal, E.N. Kermany and D. Eytan, Front. Comput. Neurosci. 3:5, 2009
• Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration (online appendix),  O.  Bobrowski, R.  Meir and Y.C. Eldar, Neural Computation, 21(5):1277-320, 2009 (preliminary NIPS version)
• Temporal Difference Based Actor Critic Learning – Convergence and Neural Implementation, D. Di Castro, S. Volkinshtein and R. Meir,  NIPS 21, pp. 385-392, MIT Press 2009.
• Delays and Oscillations in Networks of Spiking Neurons – a Two Time Scale Analysis, D. Di Castro, R. Meir and I. Yavneh, Neural Computation, 21(4):1100-24, 2009
• Selective Adaptation in Networks of Heterogeneous: Populations: Model, Simulation and Experiment,  A. Wallach, D. Eytan, S. Marom and R. Meir, PLOS Computational Biology, 4(2):e29, 2008
• Reinforcement Learning, Spike Time Dependent Plasticity and the BCM Rule, D. Baras and R. Meir, Neural Computation, vol. 19: 2245-2279, 2007
• Explaining Patterns of Neural Activity in the Primary Motor Cortex Using Spinal Cord and Limb Biomechanics Models,  E. Trainin, R. Meir and A. Karniel, J. Neurophysiology, vol. 97: 3737-3750, 2007

All publications in Google Scholar