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Conditional mean embeddings as regressors
TLDR
We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS) embeddings of conditional distributions and vector-valued regressors. Expand
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Modelling transition dynamics in MDPs with RKHS embeddings
TLDR
We propose a nonparametric approach to learning and representing transition dynamics in Markov decision processes (MDPs), which can be combined easily with dynamic programming methods for policy optimisation and value estimation. Expand
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Bandits with Delayed, Aggregated Anonymous Feedback
TLDR
We study a variant of the stochastic K-armed bandit problem, which we call “bandits with delayed, aggregated anonymous feedback”. Expand
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Movement Activity Based Classification of Animal Behaviour with an Application to Data from Cheetah (Acinonyx jubatus)
TLDR
We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations to calibrate continuously recorded activity data. Expand
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Optimal hunting conditions drive circalunar behavior of a diurnal carnivore
Foraging requirements and predation risk shape activity patterns and temporal behavior patterns widely across taxa. Although this has been extensively studied in small mammals, the influence ofExpand
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Regret Bounds for Gaussian Process Bandit Problems
TLDR
We show that the regret experienced by Bandit algorithms relative to the a posteriori optimal strategy of playing the best arm throughout based on benign assumptions about the covariance function dening the Gaussian process. Expand
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Smooth Operators
TLDR
We develop a generic approach to form smooth versions of basic mathematical operations like multiplication, composition, change of measure, and conditional expectation, among others. Expand
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Construction of approximation spaces for reinforcement learning
TLDR
We provide theoretical statements about the LSTD value approximation error and induced metric of approximation spaces constructed by SFA and the state-of-the-art methods Krylov bases and proto-value functions (PVF). Expand
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Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis
TLDR
Non-linear optimization of this unsupervised learning method generates an orthogonal basis on the unknown latent space for a given time series. Expand
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Regularized Sparse Kernel Slow Feature Analysis
TLDR
This paper develops a kernelized slow feature analysis (SFA) algorithm to extract features which encode latent variables from time series. Expand
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