Manfred Opper

	2011
47		Fabiano Ribeiro, Manfred Opper: Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models. Neural Computation 23(4): 1047-1069 (2011)
	2010
46		Manfred Opper, Andreas Ruttor, Guido Sanguinetti: Approximate inference in continuous time Gaussian-Jump processes. NIPS 2010: 1831-1839
45		Manfred Opper, Guido Sanguinetti: Learning combinatorial transcriptional dynamics from gene expression data. Bioinformatics 26(13): 1623-1629 (2010)
44		Steffen Grünewälder, Jean-Yves Audibert, Manfred Opper, John Shawe-Taylor: Regret Bounds for Gaussian Process Bandit Problems. Journal of Machine Learning Research - Proceedings Track 9: 273-280 (2010)
43		Andreas Ruttor, Manfred Opper: Approximate parameter inference in a stochastic reaction-diffusion model. Journal of Machine Learning Research - Proceedings Track 9: 669-676 (2010)
42		Michail D. Vrettas, Dan Cornford, Manfred Opper, Yuan Shen: A new variational radial basis function approximation for inference in multivariate diffusions. Neurocomputing 73(7-9): 1186-1198 (2010)
41		Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Barillec: A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. Signal Processing Systems 61(1): 51-59 (2010)
	2009
40		Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau: Switching regulatory models of cellular stress response. Bioinformatics 25(10): 1280-1286 (2009)
39		Bert Kappen, Vicenç Gómez, Manfred Opper: Optimal control as a graphical model inference problem CoRR abs/0901.0633: (2009)
38		Ulrich Paquet, Ole Winther, Manfred Opper: Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. Journal of Machine Learning Research 10: 1263-1304 (2009)
37		Manfred Opper, Cédric Archambeau: The Variational Gaussian Approximation Revisited. Neural Computation 21(3): 786-792 (2009)
	2008
36		Manfred Opper, Ulrich Paquet, Ole Winther: Improving on Expectation Propagation. NIPS 2008: 1241-1248
	2007
35		Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor: Variational Inference for Diffusion Processes. NIPS 2007
34		Manfred Opper, Guido Sanguinetti: Variational inference for Markov jump processes. NIPS 2007
33		Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor: Gaussian Process Approximations of Stochastic Differential Equations. Journal of Machine Learning Research - Proceedings Track 1: 1-16 (2007)
	2005
32		Manfred Opper: An Approximate Inference Approach for the PCA Reconstruction Error. NIPS 2005
31		Manfred Opper, Ole Winther: Expectation Consistent Approximate Inference. Journal of Machine Learning Research 6: 2177-2204 (2005)
	2004
30		Manfred Opper, Ole Winther: Approximate Inference in Probabilistic Models. ALT 2004: 494-504
29		Manfred Opper, Ole Winther: Expectation Consistent Free Energies for Approximate Inference. NIPS 2004
	2003
28		Dörthe Malzahn, Manfred Opper: Approximate Analytical Bootstrap Averages for Support Vector Classifiers. NIPS 2003
27		Manfred Opper, Ole Winther: Variational Linear Response. NIPS 2003
26		Dörthe Malzahn, Manfred Opper: Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study. Complexity 8(4): 57-63 (2003)
25		Lehel Csató, Manfred Opper, Ole Winther: Tractable inference for probabilistic data models. Complexity 8(4): 64-68 (2003)
24		Dörthe Malzahn, Manfred Opper: An Approximate Analytical Approach to Resampling Averages. Journal of Machine Learning Research 4: 1151-1173 (2003)
	2002
23		Dörthe Malzahn, Manfred Opper: A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. NIPS 2002: 327-334
22		Yoav Freund, Manfred Opper: Drifting Games and Brownian Motion. J. Comput. Syst. Sci. 64(1): 113-132 (2002)
21		Lehel Csató, Manfred Opper: Sparse On-Line Gaussian Processes. Neural Computation 14(3): 641-668 (2002)
	2001
20		Dörthe Malzahn, Manfred Opper: Learning Curves for Gaussian Processes Models: Fluctuations and Universality. ICANN 2001: 271-276
19		Lehel Csató, Dan Cornford, Manfred Opper: Online Approximations for Wind-Field Models. ICANN 2001: 300-307
18		Dörthe Malzahn, Manfred Opper: A Variational Approach to Learning Curves. NIPS 2001: 463-469
17		Manfred Opper, Robert Urbanczik: Asymptotic Universality for Learning Curves of Support Vector Machines. NIPS 2001: 479-486
16		Lehel Csató, Manfred Opper, Ole Winther: TAP Gibbs Free Energy, Belief Propagation and Sparsity. NIPS 2001: 657-663
	2000
15		Yoav Freund, Manfred Opper: Continuous Drifting Games. COLT 2000: 126-132
14		Dörthe Malzahn, Manfred Opper: Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations. NIPS 2000: 273-279
13		Lehel Csató, Manfred Opper: Sparse Representation for Gaussian Process Models. NIPS 2000: 444-450
12		Manfred Opper, Ole Winther: Gaussian Processes for Classification: Mean-Field Algorithms. Neural Computation 12(11): 2655-2684 (2000)
	1999
11		Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther: Efficient Approaches to Gaussian Process Classification. NIPS 1999: 251-257
	1998
10		Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper: Finite-Dimensional Approximation of Gaussian Processes. NIPS 1998: 218-224
9		Manfred Opper, Francesco Vivarelli: General Bounds on Bayes Errors for Regression with Gaussian Processes. NIPS 1998: 302-308
8		Manfred Opper, Ole Winther: Mean Field Methods for Classification with Gaussian Processes. NIPS 1998: 309-315
	1997
7		David Haussler, Manfred Opper: Metric Entropy and Minimax Risk in Classification. Structures in Logic and Computer Science 1997: 212-235
	1996
6		Siegfried Bös, Manfred Opper: Dynamics of Training. NIPS 1996: 141-147
5		Manfred Opper, Ole Winther: A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks. NIPS 1996: 225-231
	1995
4		David Haussler, Manfred Opper: General Bounds on the Mutual Information Between a Parameter and n Conditionally Independent Observations. COLT 1995: 402-411
	1992
3		H. Sebastian Seung, Manfred Opper, Haim Sompolinsky: Query by Committee. COLT 1992: 287-294
	1991
2		Manfred Opper, David Haussler: Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise. COLT 1991: 75-87
1		David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire: Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. NIPS 1991: 855-862

Coauthor Index

1	Cédric Archambeau	[33] [35] [37] [40] [41]
2	Jean-Yves Audibert	[44]
3	Remi Barillec	[41]
4	Siegfried Bös	[6]
5	Dan Cornford	[19] [33] [35] [41] [42]
6	Lehel Csató	[11] [13] [16] [19] [21] [25]
7	Giancarlo Ferrari-Trecate	[10]
8	Ernest Fokoué	[11]
9	Yoav Freund	[15] [22]
10	Vicenç Gómez	[39]
11	Steffen Grünewälder	[44]
12	David Haussler	[1] [2] [4] [7]
13	Hilbert J. Kappen (Bert Kappen)	[39]
14	Michael Kearns (Michael J. Kearns, Michael S. Kearns)	[1]
15	Dörthe Malzahn	[14] [18] [20] [23] [24] [26] [28]
16	Ulrich Paquet	[36] [38]
17	Fabiano Ribeiro	[47]
18	Andreas Ruttor	[40] [43] [46]
19	Guido Sanguinetti	[34] [40] [45] [46]
20	Robert E. Schapire	[1]
21	Bernhard Schottky	[11]
22	H. Sebastian Seung	[3]
23	John Shawe-Taylor	[33] [35] [41] [44]
24	Yuan Shen	[35] [41] [42]
25	Haim Sompolinsky	[3]
26	Robert Urbanczik	[17]
27	Francesco Vivarelli	[9]
28	Michail D. Vrettas	[42]
29	Christopher K. I. Williams	[10]
30	Ole Winther	[5] [8] [11] [12] [16] [25] [27] [29] [30] [31] [36] [38]

Colors in the list of coauthors

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