Mark Herbster
Mark Herbster
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Tracking the best expert
M Herbster, MK Warmuth
Machine learning 32 (2), 151-178, 1998
Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum
D Agranoff, D Fernandez-Reyes, MC Papadopoulos, SA Rojas, ...
The Lancet 368 (9540), 1012-1021, 2006
Tracking the best linear predictor
M Herbster, MK Warmuth
Journal of Machine Learning Research 1 (Sep), 281-309, 2001
Combining graph Laplacians for semi--supervised learning
A Argyriou, M Herbster, M Pontil
Advances in Neural Information Processing Systems 18, 67-74, 2005
Exponentially many local minima for single neurons
P Auer, M Herbster, MKK Warmuth
Advances in neural information processing systems 8, 316-322, 1995
Quantum machine learning: a classical perspective
C Ciliberto, M Herbster, AD Ialongo, M Pontil, A Rocchetto, S Severini, ...
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018
Online learning over graphs
M Herbster, M Pontil, L Wainer
Proceedings of the 22nd international conference on Machine learning, 305-312, 2005
Prediction on a graph with a perceptron
M Herbster, M Pontil
Advances in neural information processing systems 19, 577-584, 2006
Learning additive models online with fast evaluating kernels
M Herbster
International Conference on Computational Learning Theory, 444-460, 2001
Online prediction on large diameter graphs
M Herbster, G Lever, M Pontil
Advances in Neural Information Processing Systems, 649-656, 2009
Predicting the labelling of a graph via minimum p-seminorm interpolation
M Herbster, G Lever
NIPS Workshop 2010: Networks Across Disciplines: Theory and Applications, 2009
RNA modeling using Gibbs sampling and stochastic context free grammars.
L Grate, M Herbster, R Hughey, D Haussler, IS Mian, H Noller
Ismb 2, 138-146, 1994
Fast prediction on a tree
M Herbster, M Pontil, S Galeano
Advances in Neural Information Processing Systems 21, 657-664, 2008
Exploiting cluster-structure to predict the labeling of a graph
M Herbster
International Conference on Algorithmic Learning Theory, 54-69, 2008
Epigenetic differences in monozygotic twins discordant for major depressive disorder
K Malki, E Koritskaya, F Harris, K Bryson, M Herbster, MG Tosto
Translational psychiatry 6 (6), e839-e839, 2016
Tracking the best regressor
M Herbster, MK Warmuth
Proceedings of the eleventh annual conference on Computational learning …, 1998
Quantum linear systems algorithms: a primer
D Dervovic, M Herbster, P Mountney, S Severini, N Usher, L Wossnig
arXiv preprint arXiv:1802.08227, 2018
Service placement with provable guarantees in heterogeneous edge computing systems
S Pasteris, S Wang, M Herbster, T He
IEEE INFOCOM 2019-IEEE Conference on Computer Communications, 514-522, 2019
Online similarity prediction of networked data from known and unknown graphs
C Gentile, M Herbster, S Pasteris
Journal of Machine Learning Research 30, 662-695, 2013
Highly polygenic architecture of antidepressant treatment response: comparative analysis of SSRI and NRI treatment in an animal model of depression
K Malki, MG Tosto, H Mouriño‐Talín, S Rodríguez‐Lorenzo, O Pain, ...
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 174 …, 2017
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