RNA Modeling Using Gibbs Sampling and Stochastic Context Free Grammars. - Leslie Grate; Mark Herbster; Richard Hughey; David Haussler; I. Saira Mian; Harry Noller (ISMB, 1994)

Exponentially many local minima for single neurons. - Peter Auer; Mark Herbster; Manfred K. Warmuth (NIPS, 1995)

Tracking the Best Regressor. - Mark Herbster; Manfred K. Warmuth (COLT, 1998)

Tracking the Best Expert. - Mark Herbster; Manfred K. Warmuth (Mach. Learn., 1998)

A combined Bayes - maximum likelihood method for regression. - Alexey Ya. Chervonenkis; Alex Gammerman; Mark Herbster (Data Fusion and Perception, 2001)

Learning Additive Models Online with Fast Evaluating Kernels. - Mark Herbster (COLT/EuroCOLT, 2001)

Tracking the Best Linear Predictor. - Mark Herbster; Manfred K. Warmuth (J. Mach. Learn. Res., 2001)

Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm. - Mark Herbster (ALT, 2004)

Online learning over graphs. - Mark Herbster; Massimiliano Pontil; Lisa Wainer (ICML, 2005)

Combining Graph Laplacians for Semi-Supervised Learning. - Andreas Argyriou; Mark Herbster; Massimiliano Pontil (NIPS, 2005)

Prediction on a Graph with a Perceptron. - Mark Herbster; Massimiliano Pontil (NIPS, 2006)

Exploiting Cluster-Structure to Predict the Labeling of a Graph. - Mark Herbster (ALT, 2008)

Online Prediction on Large Diameter Graphs. - Mark Herbster; Guy Lever; Massimiliano Pontil (NIPS, 2008)

Fast Prediction on a Tree. - Mark Herbster; Massimiliano Pontil; Sergio Rojas Galeano (NIPS, 2008)

Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation. - Mark Herbster; Guy Lever (COLT, 2009)

Online Sum-Product Computation Over Trees. - Mark Herbster; Stephen Pasteris; Fabio Vitale (NIPS, 2012)

Online Similarity Prediction of Networked Data from Known and Unknown Graphs. - Claudio Gentile; Mark Herbster; Stephen Pasteris (COLT, 2013)

Online Prediction at the Limit of Zero Temperature. - Mark Herbster; Stephen Pasteris; Shaona Ghosh (NIPS, 2015)

The VC-Dimension of Similarity Hypotheses Spaces. - Mark Herbster; Paul Rubenstein; James Townsend (CoRR, 2015)

Predicting a switching sequence of graph labelings. - Mark Herbster; Stephen Pasteris; Massimiliano Pontil (J. Mach. Learn. Res., 2015)

Mistake Bounds for Binary Matrix Completion. - Mark Herbster; Stephen Pasteris; Massimiliano Pontil (NIPS, 2016)

Data distribution and scheduling for distributed analytics tasks. - Stephen Pasteris; Shiqiang Wang 001; Christian Makaya; Kevin Chan 001; Mark Herbster (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI, 2017)

Quantum machine learning: a classical perspective. - Carlo Ciliberto; Mark Herbster; Alessandro Davide Ialongo; Massimiliano Pontil; Andrea Rocchetto; Simone Severini; Leonard Wossnig (CoRR, 2017)

On Pairwise Clustering with Side Information. - Stephen Pasteris; Fabio Vitale; Claudio Gentile; Mark Herbster (CoRR, 2017)

On Similarity Prediction and Pairwise Clustering. - Stephen Pasteris; Fabio Vitale; Claudio Gentile; Mark Herbster (ALT, 2018)

Quantum linear systems algorithms: a primer. - Danial Dervovic; Mark Herbster; Peter Mountney; Simone Severini; Naïri Usher; Leonard Wossnig (CoRR, 2018)

Predicting Switching Graph Labelings with Cluster Specialists. - Mark Herbster; James Robinson (CoRR, 2018)

MaxHedge: Maximizing a Maximum Online. - Stephen Pasteris; Fabio Vitale; Kevin S. Chan; Shiqiang Wang 001; Mark Herbster (AISTATS, 2019)

Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems. - Stephen Pasteris; Shiqiang Wang 001; Mark Herbster; Ting He 001 (INFOCOM, 2019)

Online Prediction of Switching Graph Labelings with Cluster Specialists. - Mark Herbster; James Robinson (NeurIPS, 2019)

Online Multitask Learning with Long-Term Memory. - Mark Herbster; Stephen Pasteris; Lisa Tse (NeurIPS, 2020)

Online Matrix Completion with Side Information. - Mark Herbster; Stephen Pasteris; Lisa Tse (NeurIPS, 2020)

Online Learning of Facility Locations. - Stephen Pasteris; Ting He 001; Fabio Vitale; Shiqiang Wang 001; Mark Herbster (ALT, 2021)

A Gang of Adversarial Bandits. - Mark Herbster; Stephen Pasteris; Fabio Vitale; Massimiliano Pontil (NeurIPS, 2021)

Improved Regret Bounds for Tracking Experts with Memory. - James Robinson; Mark Herbster (NeurIPS, 2021)

Hierarchical Learning Algorithms for Multi-scale Expert Problems. - Lin Yang 013; Yu-Zhen Janice Chen; Mohammad H. Hajiesmaili; Mark Herbster; Don Towsley (Proc. ACM Meas. Anal. Comput. Syst., 2022)

Multi-class Graph Clustering via Approximated Effective p-Resistance. - Shota Saito; Mark Herbster (ICML, 2023)

Regret Guarantees for Adversarial Online Collaborative Filtering. - Stephen Pasteris; Fabio Vitale; Mark Herbster; Claudio Gentile (CoRR, 2023)

Generalizing p-Laplacian: spectral hypergraph theory and a partitioning algorithm. - Shota Saito; Mark Herbster (Mach. Learn., 2023)

Adversarial Online Collaborative Filtering. - Stephen Pasteris; Fabio Vitale; Mark Herbster; Claudio Gentile; André Panisson (ALT, 2024)

Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously. - Stephen Pasteris; Chris Hicks; Vasilios Mavroudis; Mark Herbster (NeurIPS, 2024)

Bandits with Abstention under Expert Advice. - Stephen Pasteris; Alberto Rumi; Maximilian Thiessen; Shota Saito; Atsushi Miyauchi 001; Fabio Vitale; Mark Herbster (NeurIPS, 2024)