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08/10/2023
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling
Channel modelling is essential to designing modern wireless communicatio...
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06/13/2023
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
Reinforcement learning (RL) has shown empirical success in various real ...
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06/01/2023
Image generation with shortest path diffusion
The field of image generation has made significant progress thanks to th...
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02/16/2023
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimizat...
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02/01/2023
Sample Complexity of Kernel-Based Q-Learning
Modern reinforcement learning (RL) often faces an enormous state-action ...
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02/01/2023
Delayed Feedback in Kernel Bandits
Black box optimisation of an unknown function from expensive and noisy e...
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07/16/2022
Kernel-based Federated Learning with Personalization
We consider federated learning with personalization, where in addition t...
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05/31/2022
Near-Optimal Collaborative Learning in Bandits
This paper introduces a general multi-agent bandit model in which each a...
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05/31/2022
Provably and Practically Efficient Neural Contextual Bandits
We consider the neural contextual bandit problem. In contrast to the exi...
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02/08/2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Kernel-based models such as kernel ridge regression and Gaussian process...
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10/28/2021
Open Problem: Tight Online Confidence Intervals for RKHS Elements
Confidence intervals are a crucial building block in the analysis of var...
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09/13/2021
Uniform Generalization Bounds for Overparameterized Neural Networks
An interesting observation in artificial neural networks is their favora...
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08/20/2021
Optimal Order Simple Regret for Gaussian Process Bandits
Consider the sequential optimization of a continuous, possibly non-conve...
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10/27/2020
A Computationally Efficient Approach to Black-box Optimization using Gaussian Process Models
We consider the sequential optimization of an unknown function from nois...
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09/15/2020
On Information Gain and Regret Bounds in Gaussian Process Bandits
Consider the sequential optimization of an expensive to evaluate and pos...
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06/09/2020
Scalable Thompson Sampling using Sparse Gaussian Process Models
Thompson Sampling (TS) with Gaussian Process (GP) models is a powerful t...
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03/11/2020
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
A framework based on iterative coordinate minimization (CM) is developed...
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03/09/2020
Amortized variance reduction for doubly stochastic objectives
Approximate inference in complex probabilistic models such as deep Gauss...
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02/12/2020
Regret Bounds for Noise-Free Bayesian Optimization
Bayesian optimisation is a powerful method for non-convex black-box opti...
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12/05/2019
Ordinal Bayesian Optimisation
Bayesian optimisation is a powerful tool to solve expensive black-box pr...
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05/16/2019
Adaptive Sensor Placement for Continuous Spaces
We consider the problem of adaptively placing sensors along an interval ...
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01/17/2019
A Random Walk Approach to First-Order Stochastic Convex Optimization
Online minimization of an unknown convex function over a convex and comp...
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07/24/2018
Decision Variance in Online Learning
Online learning has classically focused on the expected behaviour of lea...
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02/12/2018
