Mark Herbster

53 papers A* 14A 2B 5Journal 20Unranked 11
YearRankTypeTitle / Venue / Authors
2024 B conf
Adversarial Online Collaborative Filtering.
ALT
Stephen Pasteris, Fabio Vitale, Mark Herbster, Claudio Gentile, André Panisson
2024 A* conf
Bandits with Abstention under Expert Advice.
NeurIPS
Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster
2024 J jnl
Bandits with Abstention under Expert Advice.
CoRR
Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster
2024 A* conf
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously.
NeurIPS
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster
2024 J jnl
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously.
CoRR
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster
2023 J jnl
Generalizing p-Laplacian: spectral hypergraph theory and a partitioning algorithm.
Mach. Learn.
Shota Saito, Mark Herbster
2023 A* conf
Multi-class Graph Clustering via Approximated Effective p-Resistance.
ICML
Shota Saito, Mark Herbster
2023 J jnl
Multi-class Graph Clustering via Approximated Effective p-Resistance.
CoRR
Shota Saito, Mark Herbster
2023 J jnl
Regret Guarantees for Adversarial Online Collaborative Filtering.
CoRR
Stephen Pasteris, Fabio Vitale, Mark Herbster, Claudio Gentile
2022 — conf
Hierarchical Learning Algorithms for Multi-scale Expert Problems.
SIGMETRICS (Abstracts)
Lin Yang, Yu-Zhen Janice Chen, Mohammad H. Hajiesmaili, Mark Herbster, Don Towsley
2022 J jnl
Hierarchical Learning Algorithms for Multi-scale Expert Problems.
Proc. ACM Meas. Anal. Comput. Syst.
Lin Yang, Yu-Zhen Janice Chen, Mohammad H. Hajiesmaili, Mark Herbster, Don Towsley
2021 A* conf
A Gang of Adversarial Bandits.
NeurIPS
Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil
2021 A* conf
Improved Regret Bounds for Tracking Experts with Memory.
NeurIPS
James Robinson, Mark Herbster
2021 J jnl
Improved Regret Bounds for Tracking Experts with Memory.
CoRR
James Robinson, Mark Herbster
2021 B conf
Online Learning of Facility Locations.
ALT
Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster
2020 J jnl
Online Learning of Facility Locations.
CoRR
Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster
2020 A* conf
Online Matrix Completion with Side Information.
NeurIPS
Mark Herbster, Stephen Pasteris, Lisa Tse
2020 A* conf
Online Multitask Learning with Long-Term Memory.
NeurIPS
Mark Herbster, Stephen Pasteris, Lisa Tse
2020 J jnl
Online Multitask Learning with Long-Term Memory.
CoRR
Mark Herbster, Stephen Pasteris, Lisa Tse
2019 A conf
MaxHedge: Maximizing a Maximum Online.
AISTATS
Stephen Pasteris, Fabio Vitale, Kevin S. Chan, Shiqiang Wang, Mark Herbster
2019 J jnl
Online Matrix Completion with Side Information.
CoRR
Mark Herbster, Stephen Pasteris, Lisa Tse
2019 A* conf
Online Prediction of Switching Graph Labelings with Cluster Specialists.
NeurIPS
Mark Herbster, James Robinson
2019 A* conf
Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems.
INFOCOM
Stephen Pasteris, Shiqiang Wang, Mark Herbster, Ting He
2019 J jnl
Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems.
CoRR
Stephen Pasteris, Shiqiang Wang, Mark Herbster, Ting He
2018 B conf
On Similarity Prediction and Pairwise Clustering.
ALT
Stephen Pasteris, Fabio Vitale, Claudio Gentile, Mark Herbster
2018 J jnl
Predicting Switching Graph Labelings with Cluster Specialists.
CoRR
Mark Herbster, James Robinson
2018 J jnl
Quantum linear systems algorithms: a primer.
CoRR
Danial Dervovic, Mark Herbster, Peter Mountney, Simone Severini, Naïri Usher, Leonard Wossnig
2017 — conf
Data distribution and scheduling for distributed analytics tasks.
SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI
Stephen Pasteris, Shiqiang Wang, Christian Makaya, Kevin Chan, Mark Herbster
2017 J jnl
On Pairwise Clustering with Side Information.
CoRR
Stephen Pasteris, Fabio Vitale, Claudio Gentile, Mark Herbster
2017 J jnl
Quantum machine learning: a classical perspective.
CoRR
Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig
2016 — conf
Mistake Bounds for Binary Matrix Completion.
NIPS
Mark Herbster, Stephen Pasteris, Massimiliano Pontil
2015 — conf
Online Prediction at the Limit of Zero Temperature.
NIPS
Mark Herbster, Stephen Pasteris, Shaona Ghosh
2015 J jnl
Predicting a switching sequence of graph labelings.
J. Mach. Learn. Res.
Mark Herbster, Stephen Pasteris, Massimiliano Pontil
2015 J jnl
The VC-Dimension of Similarity Hypotheses Spaces.
CoRR
Mark Herbster, Paul Rubenstein, James Townsend
2013 J jnl
Online Similarity Prediction of Networked Data from Known and Unknown Graphs
CoRR
Claudio Gentile, Mark Herbster, Stephen Pasteris
2013 A* conf
Online Similarity Prediction of Networked Data from Known and Unknown Graphs.
COLT
Claudio Gentile, Mark Herbster, Stephen Pasteris
2012 — conf
Online Sum-Product Computation Over Trees.
NIPS
Mark Herbster, Stephen Pasteris, Fabio Vitale
2009 A* conf
Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation.
COLT
Mark Herbster, Guy Lever
2008 B conf
Exploiting Cluster-Structure to Predict the Labeling of a Graph.
ALT
Mark Herbster
2008 — conf
Fast Prediction on a Tree.
NIPS
Mark Herbster, Massimiliano Pontil, Sergio Rojas Galeano
2008 — conf
Online Prediction on Large Diameter Graphs.
NIPS
Mark Herbster, Guy Lever, Massimiliano Pontil
2006 — conf
Prediction on a Graph with a Perceptron.
NIPS
Mark Herbster, Massimiliano Pontil
2005 — conf
Combining Graph Laplacians for Semi-Supervised Learning.
NIPS
Andreas Argyriou, Mark Herbster, Massimiliano Pontil
2005 A* conf
Online learning over graphs.
ICML
Mark Herbster, Massimiliano Pontil, Lisa Wainer
2004 B conf
Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm.
ALT
Mark Herbster
2001 — ch.
A combined Bayes - maximum likelihood method for regression.
Data Fusion and Perception
Alexey Ya. Chervonenkis, Alex Gammerman, Mark Herbster
2001 — conf
Learning Additive Models Online with Fast Evaluating Kernels.
COLT/EuroCOLT
Mark Herbster
2001 J jnl
Tracking the Best Linear Predictor.
J. Mach. Learn. Res.
Mark Herbster, Manfred K. Warmuth
1998 J jnl
Tracking the Best Expert.
Mach. Learn.
Mark Herbster, Manfred K. Warmuth
1998 A* conf
Tracking the Best Regressor.
COLT
Mark Herbster, Manfred K. Warmuth
1995 — conf
Exponentially many local minima for single neurons.
NIPS
Peter Auer, Mark Herbster, Manfred K. Warmuth
1995 A* conf
Tracking the Best Expert.
ICML
Mark Herbster, Manfred K. Warmuth
1994 A conf
RNA Modeling Using Gibbs Sampling and Stochastic Context Free Grammars.
ISMB
Leslie Grate, Mark Herbster, Richard Hughey, David Haussler, I. Saira Mian, Harry Noller