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ORCIDRon Meir
Ronny Meir – R. S. Meir
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- affiliation: Technion - Israel Institute of Technology, Haifa, Israel
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2020 – today
- 2024
[j43]Ron Teichner
, Ron Meir, Michael Margaliot:
Analysis of the Identifying Regulation With Adversarial Surrogates Algorithm. IEEE Control. Syst. Lett. 8: 592-597 (2024)- [c54]Boaz Carmeli, Yonatan Belinkov, Ron Meir:
Concept-Best-Matching: Evaluating Compositionality In Emergent Communication. ACL (Findings) 2024: 3186-3194 - [c53]Omer Cohen, Ron Meir, Nir Weinberger:
Statistical curriculum learning: An elimination algorithm achieving an oracle risk. COLT 2024: 1142-1199 - [c52]Dror Freirich, Nir Weinberger, Ron Meir:
Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metrics. ISIT 2024: 238-243 - [i26]Dror Freirich, Nir Weinberger, Ron Meir:
Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metrics. CoRR abs/2402.02265 (2024) - [i25]Omer Cohen, Ron Meir, Nir Weinberger:
Statistical curriculum learning: An elimination algorithm achieving an oracle risk. CoRR abs/2402.13366 (2024) - [i24]Boaz Carmeli, Yonatan Belinkov, Ron Meir:
Concept-Best-Matching: Evaluating Compositionality in Emergent Communication. CoRR abs/2403.14705 (2024) - [i23]Ron Teichner, Ron Meir, Michael Margaliot:
Analysis of the Identifying Regulation with Adversarial Surrogates Algorithm. CoRR abs/2405.02953 (2024) - [i22]Lior Friedman, Ron Meir:
Data-dependent and Oracle Bounds on Forgetting in Continual Learning. CoRR abs/2406.09370 (2024) - 2023
- [c51]Boaz Carmeli, Ron Meir, Yonatan Belinkov:
Emergent Quantized Communication. AAAI 2023: 11533-11541 - [c50]Lior Friedman, Ron Meir:
Adaptive Meta-Learning via data-dependent PAC-Bayes bounds. CoLLAs 2023: 796-810 - [c49]Ron Teichner, Ron Meir:
Discrete-Time Kalman Filter Error Bounds in the Presence of Misspecified Measurements. ECC 2023: 1-7 - [c48]Dror Freirich, Tomer Michaeli, Ron Meir:
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint. NeurIPS 2023 - [c47]Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. NeurIPS 2023 - [i21]Dror Freirich, Tomer Michaeli, Ron Meir:
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint. CoRR abs/2306.02400 (2023) - [i20]Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. CoRR abs/2307.02295 (2023) - [i19]Ron Teichner, Naama Brenner, Ron Meir:
Identifying Dynamic Regulation with Adversarial Surrogates. CoRR abs/2311.17783 (2023) - 2022
- [c46]Amit Peleg, Naama Pearl, Ron Meir:
Metalearning Linear Bandits by Prior Update. AISTATS 2022: 2885-2926 - [c45]Ron Teichner, Danny Eytan, Ron Meir:
Enhancing Causal Estimation through Unlabeled Offline Data. ICFSP 2022: 147-158 - [c44]Ron Amit, Baruch Epstein, Shay Moran, Ron Meir:
Integral Probability Metrics PAC-Bayes Bounds. NeurIPS 2022 - [i18]Ilya Osadchiy, Kfir Y. Levy, Ron Meir:
Online Meta-Learning in Adversarial Multi-Armed Bandits. CoRR abs/2205.15921 (2022) - [i17]Ron Amit, Baruch Epstein, Shay Moran, Ron Meir:
Integral Probability Metrics PAC-Bayes Bounds. CoRR abs/2207.00614 (2022) - [i16]Boaz Carmeli, Ron Meir, Yonatan Belinkov:
Emergent Quantized Communication. CoRR abs/2211.02412 (2022) - 2021
- [c43]Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir:
Ensemble Bootstrapping for Q-Learning. ICML 2021: 8454-8463 - [c42]Dror Freirich, Tomer Michaeli, Ron Meir:
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space. NeurIPS 2021: 25661-25672 - [i15]Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir:
Ensemble Bootstrapping for Q-Learning. CoRR abs/2103.00445 (2021) - [i14]Dror Freirich, Tomer Michaeli, Ron Meir:
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space. CoRR abs/2107.02555 (2021) - [i13]Amit Peleg, Naama Pearl, Ron Meir:
Metalearning Linear Bandits by Prior Update. CoRR abs/2107.05320 (2021) - 2020
- [j42]Yuval Harel, Ron Meir:
Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints. Neural Comput. 32(4): 794-828 (2020) - [c41]Ron Amit, Ron Meir, Kamil Ciosek:
Discount Factor as a Regularizer in Reinforcement Learning. ICML 2020: 269-278 - [c40]Amitay Bar, Ronen Talmon, Ron Meir:
Option Discovery in the Absence of Rewards with Manifold Analysis. ICML 2020: 664-674 - [i12]Amitay Bar, Ronen Talmon, Ron Meir:
Option Discovery in the Absence of Rewards with Manifold Analysis. CoRR abs/2003.05878 (2020) - [i11]Ron Amit, Ron Meir, Kamil Ciosek:
Discount Factor as a Regularizer in Reinforcement Learning. CoRR abs/2007.02040 (2020)
2010 – 2019
- 2019
- [c39]Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar:
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. ICML 2019: 1983-1992 - [i10]Baruch Epstein, Ron Meir:
Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders. CoRR abs/1902.01449 (2019) - [i9]Or Raveh, Ron Meir:
PAC Guarantees for Concurrent Reinforcement Learning with Restricted Communication. CoRR abs/1905.09951 (2019) - 2018
- [j41]Yuval Harel, Ron Meir, Manfred Opper:
Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation. Neural Comput. 30(8) (2018) - [c38]Ron Amit, Ron Meir:
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory. ICML 2018: 205-214 - [c37]Baruch Epstein, Ron Meir, Tomer Michaeli:
Joint Autoencoders: A Flexible Meta-learning Framework. ECML/PKDD (1) 2018: 494-509 - [i8]Dror Freirich, Ron Meir, Aviv Tamar:
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. CoRR abs/1808.01960 (2018) - 2017
- [i7]Alon Hazan, Yuval Harel, Ron Meir:
Learning an attention model in an artificial visual system. CoRR abs/1701.07398 (2017) - [i6]Baruch Epstein, Ron Meir, Tomer Michaeli:
Joint auto-encoders: a flexible multi-task learning framework. CoRR abs/1705.10494 (2017) - [i5]Ron Meir, Omer Gurewitz, Asaf Cohen:
Integer Forcing: Effective SNR Distribution and Practical Block-Based Schemes. CoRR abs/1709.10037 (2017) - [i4]Ron Amit, Ron Meir:
Lifelong Learning by Adjusting Priors. CoRR abs/1711.01244 (2017) - 2016
- [j40]Gal Mishne, Ronen Talmon, Ron Meir, Jackie Schiller, Maria Lavzin, Uri Dubin, Ronald R. Coifman:
Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery. IEEE J. Sel. Top. Signal Process. 10(7): 1238-1253 (2016) - 2015
- [c36]Yuval Harel, Ron Meir, Manfred Opper:
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. NIPS 2015: 1603-1611 - 2014
- [j39]Daniel Soudry, Ron Meir:
The neuronal response at extended timescales: a linearized spiking input-output relation. Frontiers Comput. Neurosci. 8: 29 (2014) - [j38]Daniel Soudry, Ron Meir:
The neuronal response at extended timescales: long-term correlations without long-term memory. Frontiers Comput. Neurosci. 8: 35 (2014) - [c35]Daniel Soudry, Itay Hubara, Ron Meir:
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights. NIPS 2014: 963-971 - [c34]Alex K. Susemihl, Ron Meir, Manfred Opper:
Optimal Neural Codes for Control and Estimation. NIPS 2014: 2987-2995 - [i3]Alex K. Susemihl, Ron Meir, Manfred Opper:
Optimal Population Codes for Control and Estimation. CoRR abs/1406.7179 (2014) - 2012
- [j37]Daniel Soudry, Ron Meir:
Conductance-Based Neuron Models and the Slow Dynamics of Excitability. Frontiers Comput. Neurosci. 6: 4 (2012) - [j36]Aviv Tamar, Dotan Di Castro, Ron Meir:
Integrating a Partial Model into Model Free Reinforcement Learning. J. Mach. Learn. Res. 13: 1927-1966 (2012) - 2011
- [j35]Dmitry Volkinshtein, Ron Meir:
Delayed feedback control requires an internal forward model. Biol. Cybern. 105(1): 41-53 (2011) - [c33]Aviv Tamar, Dotan Di Castro, Ron Meir:
Integrating Partial Model Knowledge in Model Free RL Algorithms. ICML 2011: 305-312 - [c32]Alex K. Susemihl, Ron Meir, Manfred Opper:
Analytical Results for the Error in Filtering of Gaussian Processes. NIPS 2011: 2303-2311 - [i2]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. CoRR abs/1107.0046 (2011) - 2010
- [j34]Daniel Soudry, Ron Meir:
History-Dependent Dynamics in a Generic Model of Ion Channels - An Analytic Study. Frontiers Comput. Neurosci. 4: 3 (2010) - [j33]Steve Yaeli, Ron Meir:
Error-Based Analysis of Optimal Tuning Functions Explains Phenomena Observed in Sensory Neurons. Frontiers Comput. Neurosci. 4: 130 (2010) - [j32]Dotan Di Castro, Ron Meir:
A Convergent Online Single Time Scale Actor Critic Algorithm. J. Mach. Learn. Res. 11: 367-410 (2010)
2000 – 2009
- 2009
- [j31]Shimon Marom, Ron Meir, Erez Braun, Asaf Gal, Einat Kermany, Danny Eytan:
On the precarious path of reverse neuro-engineering. Frontiers Comput. Neurosci. 3: 5 (2009) - [j30]Dotan Di Castro, Ron Meir, Irad Yavneh:
Delays and Oscillations in Networks of Spiking Neurons: A Two-Timescale Analysis. Neural Comput. 21(4): 1100-1124 (2009) - [j29]Omer Bobrowski, Ron Meir, Yonina C. Eldar:
Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration. Neural Comput. 21(5): 1277-1320 (2009) - [j28]Dori Peleg, Ron Meir:
A sparsity driven kernel machine based on minimizing a generalization error bound. Pattern Recognit. 42(11): 2607-2614 (2009) - [i1]Dotan Di Castro, Ronny Meir:
A Convergent Online Single Time Scale Actor Critic Algorithm. CoRR abs/0909.2934 (2009) - 2008
- [j27]Avner Wallach, Danny Eytan, Shimon Marom, Ron Meir:
Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment. PLoS Comput. Biol. 4(2) (2008) - [j26]Dori Peleg, Ron Meir:
A bilinear formulation for vector sparsity optimization. Signal Process. 88(2): 375-389 (2008) - [c31]Dotan Di Castro, Dmitry Volkinshtein, Ron Meir:
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation. NIPS 2008: 385-392 - 2007
- [j25]Dorit Baras, Ron Meir:
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule. Neural Comput. 19(8): 2245-2279 (2007) - [j24]Igor Zingman, Ron Meir, Ran El-Yaniv:
Size-density spectra and their application to image classification. Pattern Recognit. 40(12): 3336-3348 (2007) - [c30]Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar:
A neural network implementing optimal state estimation based on dynamic spike train decoding. NIPS 2007: 145-152 - 2005
- [j23]George Leifman, Ron Meir, Ayellet Tal:
Semantic-oriented 3d shape retrieval using relevance feedback. Vis. Comput. 21(8-10): 865-875 (2005) - [c29]Yaakov Engel, Shie Mannor, Ron Meir:
Reinforcement learning with Gaussian processes. ICML 2005: 201-208 - [e1]Peter Auer, Ron Meir:
Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings. Lecture Notes in Computer Science 3559, Springer 2005, ISBN 3-540-26556-2 [contents] - 2004
- [j22]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. J. Artif. Intell. Res. 22: 117-142 (2004) - [j21]Yaakov Engel, Shie Mannor, Ron Meir:
The kernel recursive least-squares algorithm. IEEE Trans. Signal Process. 52(8): 2275-2285 (2004) - [c28]Arik Azran, Ron Meir:
Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers. COLT 2004: 427-441 - [c27]Dori Peleg, Ron Meir:
A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound. NIPS 2004: 1065-1072 - 2003
- [j20]Shie Mannor, Ron Meir, Tong Zhang:
Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity. J. Mach. Learn. Res. 4: 713-741 (2003) - [j19]Ron Meir, Tong Zhang:
Generalization Error Bounds for Bayesian Mixture Algorithms. J. Mach. Learn. Res. 4: 839-860 (2003) - [c26]Ilya Desyatnikov, Ron Meir:
Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. COLT 2003: 159-172 - [c25]Yaakov Engel, Shie Mannor, Ron Meir:
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning. ICML 2003: 154-161 - [c24]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Error Bounds for Transductive Learning via Compression and Clustering. NIPS 2003: 1085-1092 - [c23]Mordechai Nisenson, Ido Yariv, Ran El-Yaniv, Ron Meir:
Towards Behaviometric Security Systems: Learning to Identify a Typist. PKDD 2003: 363-374 - 2002
- [j18]Shie Mannor, Ron Meir:
On the Existence of Linear Weak Learners and Applications to Boosting. Mach. Learn. 48(1-3): 219-251 (2002) - [c22]Shie Mannor, Ron Meir, Tong Zhang:
The Consistency of Greedy Algorithms for Classification. COLT 2002: 319-333 - [c21]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Variance Optimized Bagging. ECML 2002: 60-71 - [c20]Yaakov Engel, Shie Mannor, Ron Meir:
Sparse Online Greedy Support Vector Regression. ECML 2002: 84-96 - [c19]Ron Meir, Gunnar Rätsch:
An Introduction to Boosting and Leveraging. Machine Learning Summer School 2002: 118-183 - [c18]Ron Meir, Tong Zhang:
Data-Dependent Bounds for Bayesian Mixture Methods. NIPS 2002: 319-326 - 2001
- [j17]Amir Karniel, Ron Meir, Gideon F. Inbar:
Polyhedral mixture of linear experts for many-to-one mapping inversion and multiple controllers. Neurocomputing 37(1-4): 31-49 (2001) - [j16]Amir Karniel, Ron Meir, Gideon F. Inbar:
Best estimated inverse versus inverse of the best estimator. Neural Networks 14(9): 1153-1159 (2001) - [j15]Vitaly Maiorov, Ron Meir:
Lower bounds for multivariate approximation by affine-invariant dictionaries. IEEE Trans. Inf. Theory 47(4): 1569-1575 (2001) - [c17]Shie Mannor, Ron Meir:
Geometric Bounds for Generalization in Boosting. COLT/EuroCOLT 2001: 461-472 - 2000
- [j14]Vitaly Maiorov, Ron Meir:
On the near optimality of the stochastic approximation of smooth functions by neural networks. Adv. Comput. Math. 13(1): 79-103 (2000) - [j13]Ron Meir:
Nonparametric Time Series Prediction Through Adaptive Model Selection. Mach. Learn. 39(1): 5-34 (2000) - [j12]Ron Meir, V. E. Maiorov:
On the optimality of neural-network approximation using incremental algorithms. IEEE Trans. Neural Networks Learn. Syst. 11(2): 323-337 (2000) - [c16]Ron Meir, Ran El-Yaniv, Shai Ben-David:
Localized Boosting. COLT 2000: 190-199 - [c15]Shie Mannor, Ron Meir:
Weak Learners and Improved Rates of Convergence in Boosting. NIPS 2000: 280-286
1990 – 1999
- 1999
- [j11]Ron Meir, Vitaly Maiorov:
Distortion bounds for vector quantizers with finite codebook size. IEEE Trans. Inf. Theory 45(5): 1621-1631 (1999) - [c14]Amir Karniel, Ron Meir, Gideon F. Inbar:
Exploiting the virtue of redundancy. IJCNN 1999: 2204-2209 - 1998
- [j10]Peter L. Bartlett, Vitaly Maiorov, Ron Meir:
Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. Neural Comput. 10(8): 2159-2173 (1998) - [j9]Assaf J. Zeevi, Ron Meir, Vitaly Maiorov:
Error Bounds for Functional Approximation and Estimation Using Mixtures of Experts. IEEE Trans. Inf. Theory 44(3): 1010-1025 (1998) - [j8]V. E. Maiorov, R. S. Meir:
Approximation bounds for smooth functions in C(Rd) by neural and mixture networks. IEEE Trans. Neural Networks 9(5): 969-978 (1998) - [c13]Amir Karniel, Ron Meir, Gideon F. Inbar:
Polyhedral mixture of linear experts for many-to-one mapping inversion. ESANN 1998: 155-160 - [c12]Peter L. Bartlett, Vitaly Maiorov, Ron Meir:
Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS 1998: 190-196 - [c11]Ron Meir, Vitaly Maiorov:
On the Optimality of Incremental Neural Network Algorithms. NIPS 1998: 295-301 - 1997
- [j7]Assaf J. Zeevi, Ronny Meir:
Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds. Neural Networks 10(1): 99-109 (1997) - [c10]Ron Meir:
Performance Bounds for Nonlinear Time Series Prediction. COLT 1997: 122-129 - [c9]Ron Meir:
Structural Risk Minimization for Nonparametric Time Series Prediction. NIPS 1997: 308-314 - 1996
- [c8]Joel Ratsaby, Ron Meir, Vitaly Maiorov:
Towards Robust Model Selection Using Estimation and Approximation Error Bounds. COLT 1996: 57-67 - [c7]Assaf J. Zeevi, Ron Meir, Robert J. Adler:
Time Series Prediction using Mixtures of Experts. NIPS 1996: 309-318 - 1995
- [j6]Ronny Meir, Neri Merhav:
On the Stochastic Complexity of Learning Realizable and Unrealizable Rules. Mach. Learn. 19(3): 241-261 (1995) - [j5]Ronny Meir:
Empirical Risk Minimization versus Maximum-Likelihood Estimation: A Case Study. Neural Comput. 7(1): 144-157 (1995) - 1994
- [c6]Ron Meir:
Empirical risk minimization versus maximum-likelihood estimation: A case study. ICPR (2) 1994: 295-299 - [c5]Ronny Meir:
Bias, Variance and the Combination of Least Squares Estimators. NIPS 1994: 295-302 - 1992
- [c4]Ronny Meir, José F. Fontanari:
On Learning Noisy Threshold Functions with Finite Precision Weights. COLT 1992: 280-286 - [c3]Joshua Alspector, Ronny Meir, Ben P. Yuhas, Anthony Jayakumar, D. Lippe:
A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks. NIPS 1992: 836-844 - 1991
- [j4]Ronny Meir:
On Deriving Deterministic Learning Rules from Stochastic Systems. Int. J. Neural Syst. 2(4): 283-289 (1991) - 1990
- [j3]José F. Fontanari, Ron Meir:
The Effect of Learning on the Evolution of Asexual Populations. Complex Syst. 4(4) (1990) - [j2]Pierre Baldi, Ronny Meir:
Computing with Arrays of Coupled Oscillators: An Application to Preattentive Texture Discrimination. Neural Comput. 2(4): 458-471 (1990) - [c2]Joshua Alspector, Robert B. Allen, Anthony Jayakumar, Torsten Zeppenfeld, Ronny Meir:
Relaxation Networks for Large Supervised Learning Problems. NIPS 1990: 1015-1021
1980 – 1989
- 1988
- [j1]Tal Grossman, Ronny Meir, Eytan Domany:
Learning by Choice of Internal Representations. Complex Syst. 2(5) (1988) - [c1]Tal Grossman, Ronny Meir, Eytan Domany:
Learning by Choice of Internal Representations. NIPS 1988: 73-80
Coauthor Index
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