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Number of items: 34.

Article

Agranoff, D; Fernandez-Reyes, D; Papadopoulos, MC; Rojas, SA; Herbster, M; Loosemore, A; Tarelli, E; ... Krishna, S; + view all (2006) Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. LANCET , 368 (9540) pp. 1012-1021. 10.1016/S0140-6736(06)69342-2.

Ciliberto, C; Herbster, M; Ialongo, AD; Pontil, M; Rocchetto, A; Severini, S; Wossnig, L; (2018) Quantum machine learning: a classical perspective. Proceedings Of The Royal Society A: Mathematical, Physical and Engineering Sciences , 474 (2209) , Article 20170551. 10.1098/rspa.2017.0551. Green open access
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Herbster, M; Warmuth, MK; (2001) Tracking the Best Linear Predictor. Journal of Machine Learning Research , 1 (4) pp. 281-309. 10.1162/153244301753683726. Gold open access

Herbster, M; Warmuth, MK; (1998) Tracking the best expert. Machine Learning , 32 (2) pp. 151-178. 10.1023/A:1007424614876.

Malki, K; Koritskaya, E; Harris, F; Bryson, K; Herbster, M; Tosto, MG; (2016) Epigenetic differences in monozygotic twins discordant for major depressive disorder. Translational Psychiatry , 6 , Article e839. 10.1038/tp.2016.101. Green open access
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Malki, K; Tosto, MG; Mouriño-Talín, H; Rodríguez-Lorenzo, S; Pain, O; Jumhaboy, I; Liu, T; ... Herbster, M; + view all (2016) Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 10.1002/ajmg.b.32494. Green open access
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Book chapter

Herbster, M.; (2001) Learning additive models online with fast evaluating kernels. In: Helmbold, D. and Williamson, B., (eds.) Computational Learning Theory. (pp. 444-460). Springer Verlag: Berlin/ Heidelberg, Germany.

Proceedings paper

Argyriou, A; Herbster, M; Pontil, M; (2005) Combining graph laplacians for semi-supervised learning. In: (pp. pp. 67-74).

Auer, P; Herbster, M; Warmuth, MK; others, ; (1996) Exponentially many local minima for single neurons. In: (pp. pp. 316-322). Morgan Kaufmann Publishers

Gentile, C; Herbster, M; Pasteris, S; (2013) Online Similarity Prediction of Networked Data from Known and Unknown Graphs. In: Shalev-Shwartz, S and Steinwart, I, (eds.) COLT 2013: 26th Conference on Learning Theory: Proceedings. (pp. pp. 662-695). Journal of Machine Learning Research Green open access
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Grate, L; Herbster, M; Hughey, R; Haussler, D; Mian, IS; Noller, H; (1994) RNA modeling using Gibbs sampling and stochastic context free grammars. In:

Herbster, M; (2004) Relative loss bounds and polynomial-time predictions for the K-LMS-NET algorithm. In: Ben-David, S and Case, J and Maruoka, A, (eds.) Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004: Proceedings. (pp. 309 - 323). Springer-Verlag: Berlin / Heidelberg, Germany. Green open access
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Herbster, M; (2001) Learning additive models online with fast evaluating kernels. In: (pp. pp. 444-460). Green open access
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Herbster, M; Lever, G; (2009) Predicting the labelling of a graph via minimum p-seminorm interpolation. In:

Herbster, M; Lever, G; (2009) Predicting the labelling of a graph via minimum p-seminorm interpolation. In: (Proceedings) NIPS Workshop 2010: Networks Across Disciplines: Theory and Applications. Green open access
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Herbster, M; Lever, G; Pontil, M; (2008) Online prediction on large diameter graphs. In: Koller, D and Schuurmans, D and Bengio, Y and Bottou, B, (eds.) Advances in Neural Information Processing Systems 21 (NIPS 2008). (pp. pp. 649-656). Neural Information Processing Systems Foundation Green open access
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Herbster, M; Pasteris, S; Shiqiang, W; He, T; (2019) Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems. In: Proceedings of the 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019). IEEE: Paris, France. (In press). Green open access
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Herbster, M; Pasteris, S; Vitale, F; (2012) Online Sum-Product Computation Over Trees. In: Bartlett, P and Pereira, FCN and Burges, CJC and Bottou, L and Weinberger, KQ, (eds.) (pp. pp. 2879-2887).

Herbster, M; Pasteris, S; Vitale, F; (2011) Efficient Prediction for Tree Markov Random Fields in a Streaming Model. In: (Proceedings) NIPS Workshop on Discrete Optimization (DiscML)..

Herbster, M; Pasteris, S; Vitale, F; Chan, K; Shiqiang, W; (2019) MaxHedge: Maximising a Maximum Online. In: Chaudhuri, K and Sugiyama, M, (eds.) Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS '19). (pp. pp. 1851-1859). Proceedings of Machine Learning Research: Naha, Japan. Green open access
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Herbster, M; Pontil, M; (2007) Prediction on a graph with a perceptron. In: (pp. pp. 577-584).

Herbster, M; Warmuth, MK; (1998) Tracking the best regressor. In: (pp. pp. 24-31).

Herbster, M.; Pontil, M.; Wainder, L.; (2005) Online learning over graphs. In: Dzeroski, S. and De Raedt, L. and Wrobel, S., (eds.) Proceedings of the 22nd International Conference on Machine Learning (ICML 05). (pp. pp. 305-312). ACM Press: New York, NY, USA. Green open access
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Herbster, MJ; (2008) Exploiting cluster-structure to predict the labeling of a graph. In: Freund, Y and Györfi, L and Turán, G and Zeugmann, T, (eds.) Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008: Proceedings. (pp. 54 - 69). Springer-Verlag: Berlin/Heidelberg, Germany. Green open access
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Herbster, MJ; Galeano, SR; (2007) A fast method to predict the labeling of a tree. In: Castillo, C and Davison, BD and Denoyer, L and Gallinari, P, (eds.) (Proceedings) Graph Labeling Workshop (Graphlab'07), 18th European Conference on Machine Learning (ECML'07). (pp. pp. 9-15).

Herbster, MJ; Pasteris, S; Pontil, M; (2016) Mistake Bounds for Binary Matrix Completion. In: Lee, DD and Sugiyama, M and Luxburg, UV and Guyon, I and Garnett, R and Garnett, R, (eds.) Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016). NIPS Proceedings: Barcelona, Spain. Green open access
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Herbster, MJ; Pontil, M; (2007) Prediction on a Graph with the Perceptron. In: (Proceedings) Advances in Neural Information Processing Systems (NIPS). (pp. pp. 41-48). MIT Press

Pasteris, S; Vitale, F; Gentile, C; Herbster, M; (2018) On Similarity Prediction and Pairwise Clustering. In: Proceedings of Algorithmic Learning Theory. (pp. pp. 654-681). Proceedings of Machine Learning Research (PMLR): Lanzerote, Spain. Green open access
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Report

Herbster, MJ; (2008) A Linear Lower Bound for the Perceptron for Input Sets of Constant Cardinality. (UCL Computer Science Research Notes RN/08/03 , pp. pp. 1-3 ).

Working / discussion paper

Herbster, M; Rubenstein, P; Townsend, J; The VC-Dimension of Similarity Hypotheses Spaces.

Conference item

Herbster, MJ; Pontil M, L; Rojas Galeano, S; (2008) Fast Prediction on a Tree. Presented at: NIPS 2008: Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, Canada. Green open access
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Poster

Herbster, M; (2010) A Triangle Inequality for p-Resistance. Presented at: Workshop on Networks Across Disciplines: Theory and Applications, Vancouver, B.C., Canada. Green open access
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Thesis

Pasteris, SU; (2016) Efficient algorithms for online learning over graphs. Doctoral thesis , UCL (University College London). Green open access
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Other

(2004) Relative loss bounds for predicting almost as well as any function in a union of Gaussian reproducing kernel spaces with varying widths. UNSPECIFIED

This list was generated on Sun Jun 23 16:06:57 2019 BST.