Ron Meir

Ronny Meir, R. S. Meir

	2011
62		Aviv Tamar, Dotan Di Castro, Ron Meir: Integrating Partial Model Knowledge in Model Free RL Algorithms. ICML 2011: 305-312
61		Alex K. Susemihl, Ron Meir, Manfred Opper: Analytical Results for the Error in Filtering of Gaussian Processes. NIPS 2011: 2303-2311
60		Dmitry Volkinshtein, Ron Meir: Delayed feedback control requires an internal forward model. Biological Cybernetics 105(1): 41-53 (2011)
59		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
58		Dotan Di Castro, Ron Meir: A Convergent Online Single Time Scale Actor Critic Algorithm. Journal of Machine Learning Research 11: 367-410 (2010)
	2009
57		Dotan Di Castro, Ronny Meir: A Convergent Online Single Time Scale Actor Critic Algorithm CoRR abs/0909.2934: (2009)
56		Dotan Di Castro, Ron Meir, Irad Yavneh: Delays and Oscillations in Networks of Spiking Neurons: A Two-Timescale Analysis. Neural Computation 21(4): 1100-1124 (2009)
55		Omer Bobrowski, Ron Meir, Yonina C. Eldar: Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration. Neural Computation 21(5): 1277-1320 (2009)
54		Dori Peleg, Ron Meir: A sparsity driven kernel machine based on minimizing a generalization error bound. Pattern Recognition 42(11): 2607-2614 (2009)
	2008
53		Dotan Di Castro, Dmitry Volkinshtein, Ron Meir: Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation. NIPS 2008: 385-392
52		Avner Wallach, Danny Eytan, Shimon Marom, Ron Meir: Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment. PLoS Computational Biology 4(2): (2008)
51		Dori Peleg, Ron Meir: A bilinear formulation for vector sparsity optimization. Signal Processing 88(2): 375-389 (2008)
	2007
50		Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar: A neural network implementing optimal state estimation based on dynamic spike train decoding. NIPS 2007
49		Dorit Baras, Ron Meir: Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule. Neural Computation 19(8): 2245-2279 (2007)
48		Igor Zingman, Ron Meir, Ran El-Yaniv: Size-density spectra and their application to image classification. Pattern Recognition 40(12): 3336-3348 (2007)
	2005
47		Peter Auer, Ron Meir: Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings Springer 2005
46		Yaakov Engel, Shie Mannor, Ron Meir: Reinforcement learning with Gaussian processes. ICML 2005: 201-208
45		George Leifman, Ron Meir, Ayellet Tal: Semantic-oriented 3d shape retrieval using relevance feedback. The Visual Computer 21(8-10): 865-875 (2005)
	2004
44		Arik Azran, Ron Meir: Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers. COLT 2004: 427-441
43		Dori Peleg, Ron Meir: A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound. NIPS 2004
42		Yaakov Engel, Shie Mannor, Ron Meir: The kernel recursive least-squares algorithm. IEEE Transactions on Signal Processing 52(8): 2275-2285 (2004)
41		Philip Derbeko, Ran El-Yaniv, Ron Meir: Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. J. Artif. Intell. Res. (JAIR) 22: 117-142 (2004)
	2003
40		Ilya Desyatnikov, Ron Meir: Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. COLT 2003: 159-172
39		Yaakov Engel, Shie Mannor, Ron Meir: Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning. ICML 2003: 154-161
38		Philip Derbeko, Ran El-Yaniv, Ron Meir: Error Bounds for Transductive Learning via Compression and Clustering. NIPS 2003
37		Mordechai Nisenson, Ido Yariv, Ran El-Yaniv, Ron Meir: Towards Behaviometric Security Systems: Learning to Identify a Typist. PKDD 2003: 363-374
36		Shie Mannor, Ron Meir, Tong Zhang: Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity. Journal of Machine Learning Research 4: 713-741 (2003)
35		Ron Meir, Tong Zhang: Generalization Error Bounds for Bayesian Mixture Algorithms. Journal of Machine Learning Research 4: 839-860 (2003)
	2002
34		Shie Mannor, Ron Meir, Tong Zhang: The Consistency of Greedy Algorithms for Classification. COLT 2002: 319-333
33		Philip Derbeko, Ran El-Yaniv, Ron Meir: Variance Optimized Bagging. ECML 2002: 60-71
32		Yaakov Engel, Shie Mannor, Ron Meir: Sparse Online Greedy Support Vector Regression. ECML 2002: 84-96
31		Ron Meir, Gunnar Rätsch: An Introduction to Boosting and Leveraging. Machine Learning Summer School 2002: 118-183
30		Ron Meir, Tong Zhang: Data-Dependent Bounds for Bayesian Mixture Methods. NIPS 2002: 319-326
29		Shie Mannor, Ron Meir: On the Existence of Linear Weak Learners and Applications to Boosting. Machine Learning 48(1-3): 219-251 (2002)
	2001
28		Shie Mannor, Ron Meir: Geometric Bounds for Generalization in Boosting. COLT/EuroCOLT 2001: 461-472
27		Vitaly Maiorov, Ron Meir: Lower bounds for multivariate approximation by affine-invariant dictionaries. IEEE Transactions on Information Theory 47(4): 1569-1575 (2001)
26		Amir Karniel, Ron Meir, Gideon F. Inbar: Best estimated inverse versus inverse of the best estimator. Neural Networks 14(9): 1153-1159 (2001)
25		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)
	2000
24		Ron Meir, Ran El-Yaniv, Shai Ben-David: Localized Boosting. COLT 2000: 190-199
23		Shie Mannor, Ron Meir: Weak Learners and Improved Rates of Convergence in Boosting. NIPS 2000: 280-286
22		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)
21		Ron Meir, V. E. Maiorov: On the optimality of neural-network approximation using incremental algorithms. IEEE Trans. Neural Netw. Learning Syst. 11(2): 323-337 (2000)
20		Ron Meir: Nonparametric Time Series Prediction Through Adaptive Model Selection. Machine Learning 39(1): 5-34 (2000)
	1999
19		Ron Meir, Vitaly Maiorov: Distortion bounds for vector quantizers with finite codebook size. IEEE Transactions on Information Theory 45(5): 1621-1631 (1999)
	1998
18		Amir Karniel, Ron Meir, Gideon F. Inbar: Polyhedral mixture of linear experts for many-to-one mapping inversion. ESANN 1998: 155-160
17		Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS 1998: 190-196
16		Ron Meir, Vitaly Maiorov: On the Optimality of Incremental Neural Network Algorithms. NIPS 1998: 295-301
15		Assaf J. Zeevi, Ron Meir, Vitaly Maiorov: Error Bounds for Functional Approximation and Estimation Using Mixtures of Experts. IEEE Transactions on Information Theory 44(3): 1010-1025 (1998)
14		V. E. Maiorov, R. S. Meir: Approximation bounds for smooth functions in C(Rd) by neural and mixture networks. IEEE Transactions on Neural Networks 9(5): 969-978 (1998)
13		Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. Neural Computation 10(8): 2159-2173 (1998)
	1997
12		Ron Meir: Performance Bounds for Nonlinear Time Series Prediction. COLT 1997: 122-129
11		Ron Meir: Structural Risk Minimization for Nonparametric Time Series Prediction. NIPS 1997
10		Assaf J. Zeevi, Ronny Meir: Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds. Neural Networks 10(1): 99-109 (1997)
	1996
9		Joel Ratsaby, Ron Meir, Vitaly Maiorov: Towards Robust Model Selection Using Estimation and Approximation Error Bounds. COLT 1996: 57-67
8		Assaf J. Zeevi, Ron Meir, Robert J. Adler: Time Series Prediction using Mixtures of Experts. NIPS 1996: 309-318
	1995
7		Ronny Meir, Neri Merhav: On the Stochastic Complexity of Learning Realizable and Unrealizable Rules. Machine Learning 19(3): 241-261 (1995)
	1994
6		Ronny Meir: Bias, Variance and the Combination of Least Squares Estimators. NIPS 1994: 295-302
	1992
5		Ronny Meir, José F. Fontanari: On Learning Noisy Threshold Functions with Finite Precision Weights. COLT 1992: 280-286
4		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
3		Ronny Meir: On Deriving Deterministic Learning Rules from Stochastic Systems. Int. J. Neural Syst. 2(4): 283-289 (1991)
	1990
2		Joshua Alspector, Robert B. Allen, Anthony Jayakumar, Torsten Zeppenfeld, Ronny Meir: Relaxation Networks for Large Supervised Learning Problems. NIPS 1990: 1015-1021
	1988
1		Tal Grossman, Ronny Meir, Eytan Domany: Learning by Choice of Internal Representations. NIPS 1988: 73-80

Coauthor Index

1	Robert J. Adler	[8]
2	Robert B. Allen	[2]
3	Joshua Alspector	[2] [4]
4	Peter Auer	[47]
5	Arik Azran	[44]
6	Dorit Baras	[49]
7	Peter L. Bartlett	[13] [17]
8	Shai Ben-David	[24]
9	Omer Bobrowski	[50] [55]
10	Dotan Di Castro	[53] [56] [57] [58] [62]
11	Philip Derbeko	[33] [38] [41] [59]
12	Ilya Desyatnikov	[40]
13	Eytan Domany	[1]
14	Ran El-Yaniv	[24] [33] [37] [38] [41] [48] [59]
15	Yonina C. Eldar	[50] [55]
16	Yaakov Engel	[32] [39] [42] [46]
17	Danny Eytan	[52]
18	José F. Fontanari	[5]
19	Tal Grossman	[1]
20	Gideon F. Inbar	[18] [25] [26]
21	Anthony Jayakumar	[2] [4]
22	Amir Karniel	[18] [25] [26]
23	George Leifman	[45]
24	D. Lippe	[4]
25	Vitaly Maiorov (V. E. Maiorov)	[9] [13] [14] [15] [16] [17] [19] [21] [22] [27]
26	Shie Mannor	[23] [28] [29] [32] [34] [36] [39] [42] [46]
27	Shimon Marom	[52]
28	Neri Merhav	[7]
29	Mordechai Nisenson	[37]
30	Manfred Opper	[61]
31	Dori Peleg	[43] [51] [54]
32	Joel Ratsaby	[9]
33	Gunnar Rätsch	[31]
34	Shy Shoham	[50]
35	Alex K. Susemihl	[61]
36	Ayellet Tal	[45]
37	Aviv Tamar	[62]
38	Dmitry Volkinshtein	[53] [60]
39	Avner Wallach	[52]
40	Ido Yariv	[37]
41	Irad Yavneh	[56]
42	Ben P. Yuhas	[4]
43	Assaf J. Zeevi	[8] [10] [15]
44	Torsten Zeppenfeld	[2]
45	Tong Zhang	[30] [34] [35] [36]
46	Igor Zingman	[48]

Colors in the list of coauthors

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