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2020 – today
- 2024
[c30]Stephen Pasteris, Fabio Vitale, Mark Herbster, Claudio Gentile, André Panisson:
Adversarial Online Collaborative Filtering. ALT 2024: 945-971- [i15]Stephen Pasteris, Alberto Rumi, Maximilian Thiessen
, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster:
Bandits with Abstention under Expert Advice. CoRR abs/2402.14585 (2024) - [i14]Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster:
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously. CoRR abs/2405.20824 (2024) - 2023
- [j5]Shota Saito, Mark Herbster:
Generalizing p-Laplacian: spectral hypergraph theory and a partitioning algorithm. Mach. Learn. 112(1): 241-280 (2023) - [c29]Shota Saito, Mark Herbster:
Multi-class Graph Clustering via Approximated Effective p-Resistance. ICML 2023: 29697-29733 - [i13]Stephen Pasteris, Fabio Vitale, Mark Herbster, Claudio Gentile:
Regret Guarantees for Adversarial Online Collaborative Filtering. CoRR abs/2302.05765 (2023) - [i12]Shota Saito, Mark Herbster:
Multi-class Graph Clustering via Approximated Effective p-Resistance. CoRR abs/2306.08617 (2023) - 2022
- [j4]Lin Yang, Yu-Zhen Janice Chen, Mohammad H. Hajiesmaili, Mark Herbster, Don Towsley:
Hierarchical Learning Algorithms for Multi-scale Expert Problems. Proc. ACM Meas. Anal. Comput. Syst. 6(2): 34:1-34:29 (2022) - [c28]Lin Yang, Yu-Zhen Janice Chen, Mohammad H. Hajiesmaili, Mark Herbster, Don Towsley:
Hierarchical Learning Algorithms for Multi-scale Expert Problems. SIGMETRICS (Abstracts) 2022: 71-72 - 2021
- [c27]Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster:
Online Learning of Facility Locations. ALT 2021: 1002-1050 - [c26]Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil:
A Gang of Adversarial Bandits. NeurIPS 2021: 2265-2279 - [c25]James Robinson, Mark Herbster:
Improved Regret Bounds for Tracking Experts with Memory. NeurIPS 2021: 7625-7636 - [i11]James Robinson, Mark Herbster:
Improved Regret Bounds for Tracking Experts with Memory. CoRR abs/2106.13021 (2021) - 2020
- [c24]Mark Herbster, Stephen Pasteris, Lisa Tse:
Online Multitask Learning with Long-Term Memory. NeurIPS 2020 - [c23]Mark Herbster, Stephen Pasteris, Lisa Tse:
Online Matrix Completion with Side Information. NeurIPS 2020 - [i10]Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster:
Online Learning of Facility Locations. CoRR abs/2007.02801 (2020) - [i9]Mark Herbster, Stephen Pasteris, Lisa Tse:
Online Multitask Learning with Long-Term Memory. CoRR abs/2008.07055 (2020)
2010 – 2019
- 2019
- [c22]Stephen Pasteris, Fabio Vitale, Kevin S. Chan, Shiqiang Wang, Mark Herbster:
MaxHedge: Maximizing a Maximum Online. AISTATS 2019: 1851-1859 - [c21]Stephen Pasteris, Shiqiang Wang, Mark Herbster, Ting He:
Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems. INFOCOM 2019: 514-522 - [c20]Mark Herbster, James Robinson:
Online Prediction of Switching Graph Labelings with Cluster Specialists. NeurIPS 2019: 7002-7012 - [i8]Stephen Pasteris, Shiqiang Wang, Mark Herbster, Ting He:
Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems. CoRR abs/1906.07055 (2019) - [i7]Mark Herbster, Stephen Pasteris, Lisa Tse:
Online Matrix Completion with Side Information. CoRR abs/1906.07255 (2019) - 2018
- [c19]Stephen Pasteris, Fabio Vitale, Claudio Gentile, Mark Herbster:
On Similarity Prediction and Pairwise Clustering. ALT 2018: 654-681 - [i6]Danial Dervovic, Mark Herbster, Peter Mountney, Simone Severini, Naïri Usher, Leonard Wossnig:
Quantum linear systems algorithms: a primer. CoRR abs/1802.08227 (2018) - [i5]Mark Herbster, James Robinson:
Predicting Switching Graph Labelings with Cluster Specialists. CoRR abs/1806.06439 (2018) - 2017
- [c18]Stephen Pasteris, Shiqiang Wang, Christian Makaya, Kevin Chan, Mark Herbster:
Data distribution and scheduling for distributed analytics tasks. SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017: 1-6 - [i4]Stephen Pasteris, Fabio Vitale, Claudio Gentile, Mark Herbster:
On Pairwise Clustering with Side Information. CoRR abs/1706.06474 (2017) - [i3]Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig:
Quantum machine learning: a classical perspective. CoRR abs/1707.08561 (2017) - 2016
- [c17]Mark Herbster, Stephen Pasteris, Massimiliano Pontil:
Mistake Bounds for Binary Matrix Completion. NIPS 2016: 3954-3962 - 2015
- [j3]Mark Herbster, Stephen Pasteris, Massimiliano Pontil:
Predicting a switching sequence of graph labelings. J. Mach. Learn. Res. 16: 2003-2022 (2015) - [c16]Mark Herbster, Stephen Pasteris, Shaona Ghosh:
Online Prediction at the Limit of Zero Temperature. NIPS 2015: 2935-2943 - [i2]Mark Herbster, Paul Rubenstein, James Townsend:
The VC-Dimension of Similarity Hypotheses Spaces. CoRR abs/1502.07143 (2015) - 2013
- [c15]Claudio Gentile, Mark Herbster, Stephen Pasteris:
Online Similarity Prediction of Networked Data from Known and Unknown Graphs. COLT 2013: 662-695 - [i1]Claudio Gentile, Mark Herbster, Stephen Pasteris:
Online Similarity Prediction of Networked Data from Known and Unknown Graphs. CoRR abs/1302.7263 (2013) - 2012
- [c14]Mark Herbster, Stephen Pasteris, Fabio Vitale:
Online Sum-Product Computation Over Trees. NIPS 2012: 2879-2887
2000 – 2009
- 2009
- [c13]Mark Herbster, Guy Lever:
Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation. COLT 2009 - 2008
- [c12]Mark Herbster:
Exploiting Cluster-Structure to Predict the Labeling of a Graph. ALT 2008: 54-69 - [c11]Mark Herbster, Guy Lever, Massimiliano Pontil:
Online Prediction on Large Diameter Graphs. NIPS 2008: 649-656 - [c10]Mark Herbster, Massimiliano Pontil, Sergio Rojas Galeano:
Fast Prediction on a Tree. NIPS 2008: 657-664 - 2006
- [c9]Mark Herbster, Massimiliano Pontil:
Prediction on a Graph with a Perceptron. NIPS 2006: 577-584 - 2005
- [c8]Mark Herbster, Massimiliano Pontil, Lisa Wainer:
Online learning over graphs. ICML 2005: 305-312 - [c7]Andreas Argyriou, Mark Herbster, Massimiliano Pontil:
Combining Graph Laplacians for Semi-Supervised Learning. NIPS 2005: 67-74 - 2004
- [c6]Mark Herbster:
Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm. ALT 2004: 309-323 - 2001
- [j2]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Linear Predictor. J. Mach. Learn. Res. 1: 281-309 (2001) - [c5]Mark Herbster:
Learning Additive Models Online with Fast Evaluating Kernels. COLT/EuroCOLT 2001: 444-460 - [p1]Alexey Ya. Chervonenkis, Alex Gammerman, Mark Herbster:
A combined Bayes - maximum likelihood method for regression. Data Fusion and Perception 2001: 25-49
1990 – 1999
- 1998
- [j1]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Expert. Mach. Learn. 32(2): 151-178 (1998) - [c4]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Regressor. COLT 1998: 24-31 - 1995
- [c3]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Expert. ICML 1995: 286-294 - [c2]Peter Auer, Mark Herbster, Manfred K. Warmuth:
Exponentially many local minima for single neurons. NIPS 1995: 316-322 - 1994
- [c1]Leslie Grate, Mark Herbster, Richard Hughey, David Haussler, I. Saira Mian, Harry Noller:
RNA Modeling Using Gibbs Sampling and Stochastic Context Free Grammars. ISMB 1994: 138-146
Coauthor Index
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last updated on 2024-10-23 21:22 CEST by the dblp team
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