11 citations · 13 across the 6 of their papers we have counts for
7 papers · 1 filter
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
Online Multitask Learning with Long-Term Memory
Mark Herbster, Stephen Pasteris, Lisa Tse
We introduce a novel online multitask setting. In this setting each task is partitioned into a sequence of segments that is unknown to the learner. Associated with each segment is…
Online Learning of Facility Locations
Stephen Pasteris, Ting He, Fabio Vitale +2
In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world…
Online Matrix Completion with Side Information
Mark Herbster, Stephen Pasteris, Lisa Tse
We give an online algorithm and prove novel mistake and regret bounds for online binary matrix completion with side information. The mistake bounds we prove are of the form $\tilde…
MaxHedge: Maximising a Maximum Online
Stephen Pasteris, Fabio Vitale, Kevin Chan +2
We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated wi…
On Pairwise Clustering with Side Information
Stephen Pasteris, Fabio Vitale, Claudio Gentile +1
Pairwise clustering, in general, partitions a set of items via a known similarity function. In our treatment, clustering is modeled as a transductive prediction problem. Thus rathe…