6 papers · 1 filter
Tracking the Best Strategy in an Extensive-Form Game
Stephen Pasteris, Rahul Savani, Theodore Turocy
We consider the extensive-form bandit problem where on each trial the learner plays an extensive-form game against an oblivious adversary. We focus on the notion of switching regre…
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis +1
We consider the classic problem of online convex optimisation. Whereas the notion of static regret is relevant for stationary problems, the notion of switching regret is more appro…
Fairness with Exponential Weights
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
Motivated by the need to remove discrimination in certain applications, we develop a meta-algorithm that can convert any efficient implementation of an instance of Hedge (or equiva…
Extraction Propagation
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
Running backpropagation end to end on large neural networks is fraught with difficulties like vanishing gradients and degradation. In this paper we present an alternative architect…
Bandits with Abstention under Expert Advice
Stephen Pasteris, Alberto Rumi, Maximilian Thiessen +4
We study the classic problem of prediction with expert advice under bandit feedback. Our model assumes that one action, corresponding to the learner's abstention from play, has no…
Adversarial Online Collaborative Filtering
Stephen Pasteris, Fabio Vitale, Mark Herbster +2
We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot b…