1 code implementation • 16 Oct 2023 • Kieran Wood, Samuel Kessler, Stephen J. Roberts, Stefan Zohren
X-Trend is able to quickly adapt to new financial regimes with a Sharpe ratio increase of 18. 9% over a neural forecaster and 10-fold over a conventional Time-series Momentum strategy during the turbulent market period from 2018 to 2023.
no code implementations • 29 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.
1 code implementation • 4 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.
no code implementations • 15 Dec 2022 • Davide Pigoli, Kieran Baker, Jobie Budd, Lorraine Butler, Harry Coppock, Sabrina Egglestone, Steven G. Gilmour, Chris Holmes, David Hurley, Radka Jersakova, Ivan Kiskin, Vasiliki Koutra, Jonathon Mellor, George Nicholson, Joe Packham, Selina Patel, Richard Payne, Stephen J. Roberts, Björn W. Schuller, Ana Tendero-Cañadas, Tracey Thornley, Alexander Titcomb
Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings.
2 code implementations • 29 Nov 2022 • Samuel Kessler, Mateusz Ostaszewski, Michał Bortkiewicz, Mateusz Żarski, Maciej Wołczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Miłoś
World models power some of the most efficient reinforcement learning algorithms.
no code implementations • 23 Oct 2022 • Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette
We then present CASCADE, a novel approach for self-supervised exploration in this new setting.
no code implementations • 8 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.
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.
1 code implementation • 14 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.
no code implementations • 8 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.
no code implementations • 14 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.
1 code implementation • 5 Jun 2021 • Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, Stephen J. Roberts
In this paper we formalize this "interference" as distinct from the problem of forgetting.
1 code implementation • 27 Jan 2021 • Philip J. Ball, Stephen J. Roberts
Two popular approaches to model-free continuous control tasks are SAC and TD3.
no code implementations • 7 Jan 2021 • Arno Blaas, Stephen J. Roberts
It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks.
no code implementations • 26 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.
no code implementations • 2 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.
1 code implementation • 14 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.
no code implementations • 22 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.
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.
1 code implementation • 5 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".
1 code implementation • 29 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.
1 code implementation • 24 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.
no code implementations • 29 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.
no code implementations • 4 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.
no code implementations • 9 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.
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.
1 code implementation • 10 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.
no code implementations • 28 Mar 2018 • Zhikuan Zhao, Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, Joseph F. Fitzsimons
Gaussian processes (GPs) are important models in supervised machine learning.
no code implementations • 9 Mar 2018 • Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts
We propose practical extensions to Bayesian optimization for solving dynamic problems.
1 code implementation • 22 Feb 2018 • Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
In systems of multiple agents, identifying the cause of observed agent dynamics is challenging.
no code implementations • 4 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.
no code implementations • 7 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.
no code implementations • 2 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.
no code implementations • 27 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.
no code implementations • 23 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.
1 code implementation • 13 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.
1 code implementation • 12 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
no code implementations • 9 Oct 2015 • Yves-Laurent Kom Samo, Stephen J. Roberts
In this paper we introduce a novel online time series forecasting model we refer to as the pM-GP filter.
no code implementations • 8 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.
no code implementations • 20 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.
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.
no code implementations • 2 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.
no code implementations • 30 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.
no code implementations • 25 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.
no code implementations • 18 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.
no code implementations • NeurIPS 2012 • Michael Osborne, Roman Garnett, Zoubin Ghahramani, David K. Duvenaud, Stephen J. Roberts, Carl E. Rasmussen
Numerical integration is an key component of many problems in scientific computing, statistical modelling, and machine learning.