Search Results for author: Stephen J. Roberts

Found 46 papers, 16 papers with code

Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

2 code implementations16 Oct 2023 Kieran Wood, Samuel Kessler, Stephen J. Roberts, Stefan Zohren

To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes.

Few-Shot Learning Time Series

SANE: The phases of gradient descent through Sharpness Adjusted Number of Effective parameters

no code implementations29 May 2023 Lawrence Wang, Stephen J. Roberts

By considering the local curvature, we propose Sharpness Adjusted Number of Effective parameters (SANE), a measure of effective dimensionality for the quality of solutions.

On Sequential Bayesian Inference for Continual Learning

1 code implementation4 Jan 2023 Samuel Kessler, Adam Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts

Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.

Bayesian Inference Continual Learning +1

On-the-fly Strategy Adaptation for ad-hoc Agent Coordination

no code implementations8 Mar 2022 Jaleh Zand, Jack Parker-Holder, Stephen J. Roberts

Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world.

Game of Hanabi Multi-agent Reinforcement Learning

Can Sequential Bayesian Inference Solve Continual Learning?

no code implementations pproximateinference AABI Symposium 2022 Samuel Kessler, Adam D. Cobb, Stefan Zohren, Stephen J. Roberts

Previous work in Continual Learning (CL) has used sequential Bayesian inference to prevent forgetting and accumulate knowledge from previous tasks.

Bayesian Inference Continual Learning +1

HumBugDB: A Large-scale Acoustic Mosquito Dataset

1 code implementation14 Oct 2021 Ivan Kiskin, Marianne Sinka, Adam D. Cobb, Waqas Rafique, Lawrence Wang, Davide Zilli, Benjamin Gutteridge, Rinita Dam, Theodoros Marinos, Yunpeng Li, Dickson Msaky, Emmanuel Kaindoa, Gerard Killeen, Eva Herreros-Moya, Kathy J. Willis, Stephen J. Roberts

Our extensive dataset is both challenging to machine learning researchers focusing on acoustic identification, and critical to entomologists, geo-spatial modellers and other domain experts to understand mosquito behaviour, model their distribution, and manage the threat they pose to humans.

Cultural Vocal Bursts Intensity Prediction

Revisiting Design Choices in Offline Model-Based Reinforcement Learning

no code implementations8 Oct 2021 Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, Stephen J. Roberts

Significant progress has been made recently in offline model-based reinforcement learning, approaches which leverage a learned dynamics model.

Bayesian Optimization Model-based Reinforcement Learning +2

Marginalising over Stationary Kernels with Bayesian Quadrature

no code implementations14 Jun 2021 Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen J. Roberts

Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates.

OffCon$^3$: What is state of the art anyway?

1 code implementation27 Jan 2021 Philip J. Ball, Stephen J. Roberts

Two popular approaches to model-free continuous control tasks are SAC and TD3.

Continuous Control

Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks

no code implementations26 Mar 2020 Bernardo Pérez Orozco, Stephen J. Roberts

Recurrent neural networks (RNNs) are state-of-the-art in several sequential learning tasks, but they often require considerable amounts of data to generalise well.

regression Time Series +1

Implicit Priors for Knowledge Sharing in Bayesian Neural Networks

no code implementations2 Dec 2019 Jack K. Fitzsimons, Sebastian M. Schmon, Stephen J. Roberts

Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties like uncertainty estimation, model robustness and regularisation.

Transfer Learning

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo

1 code implementation14 Oct 2019 Adam D. Cobb, Atılım Güneş Baydin, Andrew Markham, Stephen J. Roberts

We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians.

Bayesian Inference

Adaptive Configuration Oracle for Online Portfolio Selection Methods

no code implementations22 Aug 2019 Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts

Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data.

Bayesian Optimization

Bayesian Optimisation over Multiple Continuous and Categorical Inputs

2 code implementations ICML 2020 Binxin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J. Roberts

Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges.

Bayesian Optimisation Multi-Armed Bandits

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources

1 code implementation5 Apr 2019 Edwin Simpson, Steven Reece, Stephen J. Roberts

Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial "heatmap".

Classification Disaster Response +1

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

1 code implementation29 Jan 2019 Ahsan S. Alvi, Binxin Ru, Jan Calliess, Stephen J. Roberts, Michael A. Osborne

Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle.

Bayesian Optimisation

Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels

1 code implementation24 Jan 2019 Matthew Willetts, Stephen J. Roberts, Christopher C. Holmes

It could easily be the case that some classes of data are found only in the unlabelled dataset -- perhaps the labelling process was biased -- so we do not have any labelled examples to train on for some classes.


BCCNet: Bayesian classifier combination neural network

no code implementations29 Nov 2018 Olga Isupova, Yunpeng Li, Danil Kuzin, Stephen J. Roberts, Katherine Willis, Steven Reece

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements.

BIG-bench Machine Learning Decision Making +1

Automated bird sound recognition in realistic settings

no code implementations4 Sep 2018 Timos Papadopoulos, Stephen J. Roberts, Katherine J. Willis

Our use of generic training data and our investigation of probabilistic classification methodologies that can flexibly address the variable number of candidate species/classes that are expected to be encountered in the field, directly contribute to the development of a practical bird sound identification system with potentially global application.

Classification General Classification

Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting

no code implementations9 Jul 2018 Sid Ghoshal, Stephen J. Roberts

Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts.

Time Series Time Series Analysis

Optimization, fast and slow: optimally switching between local and Bayesian optimization

1 code implementation ICML 2018 Mark McLeod, Michael A. Osborne, Stephen J. Roberts

We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between multiple alternative acquisition functions and traditional local optimization at each step.

Bayesian Optimization

Loss-Calibrated Approximate Inference in Bayesian Neural Networks

1 code implementation10 May 2018 Adam D. Cobb, Stephen J. Roberts, Yarin Gal

Current approaches in approximate inference for Bayesian neural networks minimise the Kullback-Leibler divergence to approximate the true posterior over the weights.

Autonomous Driving Semantic Segmentation

Bayesian Optimization for Dynamic Problems

no code implementations9 Mar 2018 Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts

We propose practical extensions to Bayesian optimization for solving dynamic problems.

Bayesian Optimization

Inferring agent objectives at different scales of a complex adaptive system

no code implementations4 Dec 2017 Dieter Hendricks, Adam Cobb, Richard Everett, Jonathan Downing, Stephen J. Roberts

It has been suggested that multiple agent classes operate in this system, with a non-trivial hierarchy of top-down and bottom-up causation classes with different effective models governing each level.

Learning from lions: inferring the utility of agents from their trajectories

no code implementations7 Sep 2017 Adam D. Cobb, Andrew Markham, Stephen J. Roberts

We build a model using Gaussian processes to infer a spatio-temporal vector field from observed agent trajectories.

Decision Making Gaussian Processes

A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes

no code implementations2 May 2017 Syed Ali Asad Rizvi, Stephen J. Roberts, Michael A. Osborne, Favour Nyikosa

In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series.

regression Time Series +1

Optimal client recommendation for market makers in illiquid financial products

no code implementations27 Apr 2017 Dieter Hendricks, Stephen J. Roberts

The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers.


Distribution of Gaussian Process Arc Lengths

no code implementations23 Mar 2017 Justin D. Bewsher, Alessandra Tosi, Michael A. Osborne, Stephen J. Roberts

We fill the gap in the existing literature by deriving the moments of the arc length for a stationary GP with multiple output dimensions.

Practical Bayesian Optimization for Variable Cost Objectives

1 code implementation13 Mar 2017 Mark McLeod, Michael A. Osborne, Stephen J. Roberts

We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines the tradeoff between evaluation cost and the fidelity of the evaluations.

Bayesian Optimization

GPz: Non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts

1 code implementation12 Apr 2016 Ibrahim A. Almosallam, Matt J. Jarvis, Stephen J. Roberts

The predictive variance of the model takes into account both the variance due to data density and photometric noise.

Instrumentation and Methods for Astrophysics I.2.6

A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression

no code implementations8 Sep 2015 Arnold Salas, Stephen J. Roberts, Michael A. Osborne

Online Passive-Aggressive (PA) learning is a class of online margin-based algorithms suitable for a wide range of real-time prediction tasks, including classification and regression.

General Classification regression +1

A Sparse Gaussian Process Framework for Photometric Redshift Estimation

no code implementations20 May 2015 Ibrahim A. Almosallam, Sam N. Lindsay, Matt J. Jarvis, Stephen J. Roberts

The proposed framework reaches a mean absolute $\Delta z = 0. 0026(1+z_\textrm{s})$, over the redshift range of $0 \le z_\textrm{s} \le 2$ on the simulated data, and $\Delta z = 0. 0178(1+z_\textrm{s})$ over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature.

Gaussian Processes Photometric Redshift Estimation +1

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

no code implementations NeurIPS 2014 Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts

We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC) benchmarks.

Active Learning Numerical Integration

Variational Inference for Gaussian Process Modulated Poisson Processes

no code implementations2 Nov 2014 Chris Lloyd, Tom Gunter, Michael A. Osborne, Stephen J. Roberts

We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes.

Astronomy Bayesian Inference +2

Automated Machine Learning on Big Data using Stochastic Algorithm Tuning

no code implementations30 Jul 2014 Thomas Nickson, Michael A. Osborne, Steven Reece, Stephen J. Roberts

However, the state of the art in Bayesian optimisation is incapable of scaling to the large number of evaluations of algorithm performance required to fit realistic models to complex, big data.

Bayesian Optimisation Benchmarking +3

Efficient Bayesian Nonparametric Modelling of Structured Point Processes

no code implementations25 Jul 2014 Tom Gunter, Chris Lloyd, Michael A. Osborne, Stephen J. Roberts

This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently.

Point Processes

Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems

no code implementations18 Nov 2013 Jan-Peter Calliess, Antonis Papachristodoulou, Stephen J. Roberts

In contrast to previous work that has used stochastic processes for identification, we leverage the structural knowledge afforded by Lagrangian mechanics and learn the drift and control input matrix functions of the control-affine system separately.

Cannot find the paper you are looking for? You can Submit a new open access paper.