56 citations · 129 across the 9 of their papers we have counts for
9 papers
Oblivious Data for Fairness with Kernels
Steffen Grünewälder, Azadeh Khaleghi
We investigate the problem of algorithmic fairness in the case where sensitive and non-sensitive features are available and one aims to generate new, `oblivious', features that clo…
Recovering Bandits
Ciara Pike-Burke, Steffen Grünewälder
We study the recovering bandits problem, a variant of the stochastic multi-armed bandit problem where the expected reward of each arm varies according to some unknown function of t…
Bandits with Delayed, Aggregated Anonymous Feedback
Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari +1
We study a variant of the stochastic -armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a…
Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces
Stephen Page, Steffen Grünewälder
We study kernel least-squares estimation under a norm constraint. This form of regularisation is known as Ivanov regularisation and it provides better control of the norm of the es…
Approximations of the Restless Bandit Problem
Steffen Grunewalder, Azadeh Khaleghi
The multi-armed restless bandit problem is studied in the case where the pay-off distributions are stationary -mixing. This version of the problem provides a more realistic mode…
Modelling transition dynamics in MDPs with RKHS embeddings
Steffen Grunewalder, Guy Lever, Luca Baldassarre +2
We propose a new, nonparametric approach to learning and representing transition dynamics in Markov decision processes (MDPs), which can be combined easily with dynamic programming…
Conditional mean embeddings as regressors - supplementary
Steffen Grünewälder, Guy Lever, Luca Baldassarre +3
We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS) embeddings of conditional distributions and vector-valued regressors. This connection introduces a nat…
Modeling transition dynamics in MDPs with RKHS embeddings of conditional distributions
Steffen Grünewälder, Luca Baldassarre, Massimiliano Pontil +2
We propose a new, nonparametric approach to estimating the value function in reinforcement learning. This approach makes use of a recently developed representation of conditional d…
The Optimal Unbiased Value Estimator and its Relation to LSTD, TD and MC
Steffen Grünewälder, Klaus Obermayer
In this analytical study we derive the optimal unbiased value estimator (MVU) and compare its statistical risk to three well known value estimators: Temporal Difference learning (T…