| Tracking the best expert M Herbster, MK Warmuth Machine learning 32 (2), 151-178, 1998 | 775 | 1998 |
| 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 | 505 | 2018 |
| 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 | 352 | 2006 |
| Exponentially many local minima for single neurons P Auer, M Herbster, MKK Warmuth Advances in neural information processing systems 8, 1995 | 243 | 1995 |
| Tracking the best linear predictor M Herbster, MK Warmuth Journal of Machine Learning Research 1 (281-309), 10-1162, 2001 | 235 | 2001 |
| Combining graph Laplacians for semi--supervised learning A Argyriou, M Herbster, M Pontil Advances in Neural Information Processing Systems 18, 2005 | 198 | 2005 |
| 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 | 170 | 2019 |
| Quantum linear systems algorithms: a primer D Dervovic, M Herbster, P Mountney, S Severini, N Usher, L Wossnig arXiv preprint arXiv:1802.08227, 2018 | 128 | 2018 |
| Online learning over graphs M Herbster, M Pontil, L Wainer Proceedings of the 22nd international conference on Machine learning, 305-312, 2005 | 126 | 2005 |
| Prediction on a graph with a perceptron M Herbster, M Pontil Advances in neural information processing systems 19, 2006 | 99 | 2006 |
| Learning additive models online with fast evaluating kernels M Herbster Computational Learning Theory: 14th Annual Conference on Computational …, 2001 | 73 | 2001 |
| 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 | 68 | 2009 |
| Online prediction on large diameter graphs M Herbster, G Lever, M Pontil Advances in Neural Information Processing Systems 21, 2008 | 61 | 2008 |
| Fast prediction on a tree M Herbster, M Pontil, S Galeano Advances in Neural Information Processing Systems 21, 2008 | 56 | 2008 |
| 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 | 55 | 1994 |
| 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 | 50 | 2016 |
| Exploiting cluster-structure to predict the labeling of a graph M Herbster International Conference on Algorithmic Learning Theory, 54-69, 2008 | 38 | 2008 |
| Tracking the best regressor M Herbster, MK Warmuth Proceedings of the eleventh annual conference on Computational learning …, 1998 | 33 | 1998 |
| Data encoding and classification M Herbster, P Mountney, S Piat, S Severini US Patent 10,977,558, 2021 | 25 | 2021 |
| Online similarity prediction of networked data from known and unknown graphs C Gentile, M Herbster, S Pasteris Conference on Learning Theory, 662-695, 2013 | 22 | 2013 |
