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05/29/2023
SANE: The phases of gradient descent through Sharpness Adjusted Number of Effective parameters
Modern neural networks are undeniably successful. Numerous studies have ...
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01/04/2023
On Sequential Bayesian Inference for Continual Learning
Sequential Bayesian inference can be used for continual learning to prev...
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12/15/2022
Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19
Since early in the coronavirus disease 2019 (COVID-19) pandemic, there h...
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11/29/2022
The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning
We study the use of model-based reinforcement learning methods, in parti...
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10/23/2022
Learning General World Models in a Handful of Reward-Free Deployments
Building generally capable agents is a grand challenge for deep reinforc...
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03/08/2022
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination
Training agents in cooperative settings offers the promise of AI agents ...
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10/14/2021
HumBugDB: A Large-scale Acoustic Mosquito Dataset
This paper presents the first large-scale multi-species dataset of acous...
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10/08/2021
Revisiting Design Choices in Model-Based Offline Reinforcement Learning
Offline reinforcement learning enables agents to leverage large pre-coll...
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06/14/2021
Marginalising over Stationary Kernels with Bayesian Quadrature
Marginalising over families of Gaussian Process kernels produces flexibl...
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06/05/2021
Same State, Different Task: Continual Reinforcement Learning without Interference
Continual Learning (CL) considers the problem of training an agent seque...
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01/27/2021
OffCon^3: What is state of the art anyway?
Two popular approaches to model-free continuous control tasks are SAC an...
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01/07/2021
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks
It is desirable, and often a necessity, for machine learning models to b...
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03/26/2020
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks
Recurrent neural networks (RNNs) are state-of-the-art in several sequent...
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12/02/2019
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
Bayesian interpretations of neural network have a long history, dating b...
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10/14/2019
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
We introduce a recent symplectic integration scheme derived for solving ...
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08/22/2019
Adaptive Configuration Oracle for Online Portfolio Selection Methods
Financial markets are complex environments that produce enormous amounts...
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06/20/2019
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of black-box problems that comprise both continuo...
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04/05/2019
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Unstructured data from diverse sources, such as social media and aerial ...
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01/29/2019
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperp...
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01/24/2019
Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels
We introduce semi-unsupervised learning, an extreme case of semi-supervi...
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11/29/2018
BCCNet: Bayesian classifier combination neural network
Machine learning research for developing countries can demonstrate clear...
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11/15/2018
A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback
Computing Nash equilibria for strategic multi-agent systems is challengi...
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09/04/2018
Automated bird sound recognition in realistic settings
We evaluated the effectiveness of an automated bird sound identification...
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05/22/2018
Optimization, fast and slow: optimally switching between local and Bayesian optimization
We develop the first Bayesian Optimization algorithm, BLOSSOM, which sel...
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05/10/2018
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
Current approaches in approximate inference for Bayesian neural networks...
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03/28/2018
Quantum algorithms for training Gaussian Processes
Gaussian processes (GPs) are important models in supervised machine lear...
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03/09/2018
Bayesian Optimization for Dynamic Problems
We propose practical extensions to Bayesian optimization for solving dyn...
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02/22/2018
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent d...
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12/04/2017
Inferring agent objectives at different scales of a complex adaptive system
We introduce a framework to study the effective objectives at different ...
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09/07/2017
Learning from lions: inferring the utility of agents from their trajectories
We build a model using Gaussian processes to infer a spatio-temporal vec...
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05/02/2017
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes
In this paper we use Gaussian Process (GP) regression to propose a novel...
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04/27/2017
Optimal client recommendation for market makers in illiquid financial products
The process of liquidity provision in financial markets can result in pr...
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03/23/2017
Distribution of Gaussian Process Arc Lengths
We present the first treatment of the arc length of the Gaussian Process...
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03/13/2017
Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for black-box function...
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10/09/2015
p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting
In this paper we introduce a novel online time series forecasting model ...
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09/08/2015
A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression
Online Passive-Aggressive (PA) learning is a class of online margin-base...
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11/03/2014
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic mod...
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11/02/2014
Variational Inference for Gaussian Process Modulated Poisson Processes
We present the first fully variational Bayesian inference scheme for con...
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07/30/2014
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
We introduce a means of automating machine learning (ML) for big data ta...
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07/25/2014
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This paper presents a Bayesian generative model for dependent Cox point ...
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11/18/2013
Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems
This work proposes a new method for simultaneous probabilistic identific...
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12/12/2012