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Found 13 papers, 6 papers with code
Ensemble sampling for linear bandits: small ensembles suffice
We provide the first useful and rigorous analysis of ensemble sampling for the stochastic linear bandit setting.
Exploration via linearly perturbed loss minimisation
We show that, for the case of generalised linear bandits, EVILL reduces to perturbed history exploration (PHE), a method where exploration is done by training on randomly perturbed rewards.
Stochastic Gradient Descent for Gaussian Processes Done Right
We study the optimisation problem associated with Gaussian process regression using squared loss.
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems.
Sampling-based inference for large linear models, with application to linearised Laplace
Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method.
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community.
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior
We show that for neural networks (NN) with normalisation layers, i. e. batch norm, layer norm, or group norm, the Laplace model evidence does not approximate the volume of a posterior mode and is thus unsuitable for model selection.
Bandit optimisation of functions in the Matérn kernel RKHS
We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a Mat\'ern kernel with smoothness parameter $\nu$ over the domain $[0, 1]^d$ under noisy bandit feedback.
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning.
Learning to Drive in a Day
We demonstrate the first application of deep reinforcement learning to autonomous driving.
Learning a Generative Model for Validity in Complex Discrete Structures
This validator provides insight as to how individual sequence elements influence the validity of the overall sequence, and can be used to constrain sequence based models to generate valid sequences -- and thus faithfully model discrete objects.
Actively Learning what makes a Discrete Sequence Valid
As a step towards solving this problem, we propose to learn a deep recurrent validator model.
Probabilistic structure discovery in time series data
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models.
