Stephen Roberts

research

∙ 08/23/2023

Learning to Learn Financial Networks for Optimising Momentum Strategies

Network momentum provides a novel type of risk premium, which exploits t...

0 Xingyue Pu, et al. ∙

research

∙ 08/22/2023

Network Momentum across Asset Classes

We investigate the concept of network momentum, a novel trading signal d...

0 Xingyue Pu, et al. ∙

research

∙ 07/22/2023

The instabilities of large learning rate training: a loss landscape view

Modern neural networks are undeniably successful. Numerous works study h...

0 Lawrence Wang, et al. ∙

research

∙ 06/24/2023

G-TRACER: Expected Sharpness Optimization

We propose a new regularization scheme for the optimization of deep lear...

0 John Williams, et al. ∙

research

∙ 03/20/2023

An energy stable discontinuous Galerkin spectral element method for the linearized Serre equations

We derive well-posed boundary conditions for the linearized Serre equati...

0 Kenny Wiratama, et al. ∙

research

∙ 02/20/2023

Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies

We introduce Spatio-Temporal Momentum strategies, a class of models that...

0 Wee Ling Tan, et al. ∙

research

∙ 12/15/2022

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers

Recent work has reported that AI classifiers trained on audio recordings...

0 Harry Coppock, et al. ∙

research

∙ 12/15/2022

A large-scale and PCR-referenced vocal audio dataset for COVID-19

The UK COVID-19 Vocal Audio Dataset is designed for the training and eva...

0 Jobie Budd, et al. ∙

research

∙ 08/21/2022

Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity

Cross-sectional strategies are a classical and popular trading style, wi...

0 Daniel Poh, et al. ∙

research

∙ 05/13/2022

The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, Mosquitoes

The ACM Multimedia 2022 Computational Paralinguistics Challenge addresse...

0 Björn W. Schuller, et al. ∙

research

∙ 12/16/2021

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

Deep learning architectures, specifically Deep Momentum Networks (DMNs) ...

0 Kieran Wood, et al. ∙

research

∙ 12/07/2021

A Bayesian take on option pricing with Gaussian processes

Local volatility is a versatile option pricing model due to its state de...

0 Martin Tegner, et al. ∙

research

∙ 10/21/2021

One-Shot Transfer Learning of Physics-Informed Neural Networks

Solving differential equations efficiently and accurately sits at the he...

0 Shaan Desai, et al. ∙

research

∙ 10/11/2021

Robust and Scalable SDE Learning: A Functional Perspective

Stochastic differential equations provide a rich class of flexible gener...

0 Scott Cameron, et al. ∙

research

∙ 07/16/2021

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

Accurately learning the temporal behavior of dynamical systems requires ...

0 Shaan Desai, et al. ∙

research

∙ 06/30/2021

Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL

Despite a series of recent successes in reinforcement learning (RL), man...

3 Jack Parker-Holder, et al. ∙

research

∙ 06/04/2021

Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective

ResNets constrained to be bi-Lipschitz, that is, approximately distance ...

0 Lewis Smith, et al. ∙

research

∙ 05/28/2021

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Momentum strategies are an important part of alternative investments and...

0 Kieran Wood, et al. ∙

research

∙ 05/20/2021

Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures

The performance of a cross-sectional currency strategy depends crucially...

0 Daniel Poh, et al. ∙

research

∙ 04/12/2021

Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment

Reinforcement learning from large-scale offline datasets provides us wit...

0 Philip J. Ball, et al. ∙

research

∙ 04/07/2021

Adversarial Robustness Guarantees for Gaussian Processes

Gaussian processes (GPs) enable principled computation of model uncertai...

6 Andrea Patane, et al. ∙

research

∙ 12/13/2020

Building Cross-Sectional Systematic Strategies By Learning to Rank

The success of a cross-sectional systematic strategy depends critically ...

0 Daniel Poh, et al. ∙

research

∙ 11/15/2020

Explaining the Adaptive Generalisation Gap

We conjecture that the reason for the difference in generalisation betwe...

0 Diego Granziol, et al. ∙

research

∙ 08/07/2020

SafePILCO: a software tool for safe and data-efficient policy synthesis

SafePILCO is a software tool for safe and data-efficient policy search w...

0 Kyriakos Polymenakos, et al. ∙

research

∙ 07/14/2020

Explicit Regularisation in Gaussian Noise Injections

We study the regularisation induced in neural networks by Gaussian noise...

0 Alexander Camuto, et al. ∙

research

∙ 07/14/2020

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders

We make inroads into understanding the robustness of Variational Autoenc...

16 Alexander Camuto, et al. ∙

research

∙ 07/14/2020

Relaxed-Responsibility Hierarchical Discrete VAEs

Successfully training Variational Autoencoders (VAEs) with a hierarchy o...

0 Matthew Willetts, et al. ∙

research

∙ 06/21/2020

On Optimism in Model-Based Reinforcement Learning

The principle of optimism in the face of uncertainty is prevalent throug...

0 Aldo Pacchiano, et al. ∙

research

∙ 05/27/2020

Deep Learning for Portfolio Optimisation

We adopt deep learning models to directly optimise the portfolio Sharpe ...

0 Zihao Zhang, et al. ∙

research

∙ 04/28/2020

VIGN: Variational Integrator Graph Networks

Rich, physically-informed inductive biases play an imperative role in ac...

0 Shaan Desai, et al. ∙

research

∙ 04/08/2020

Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)

Generative Adversarial Networks (GANs) have gained significant attention...

0 Jaleh Zand, et al. ∙

research

∙ 03/02/2020

Iterate Averaging Helps: An Alternative Perspective in Deep Learning

Iterate averaging has a rich history in optimisation, but has only very ...

0 Diego Granziol, et al. ∙

research

∙ 02/18/2020

Learning Bijective Feature Maps for Linear ICA

Separating high-dimensional data like images into independent latent fac...

19 Alexander Camuto, et al. ∙

research

∙ 02/07/2020

Ready Policy One: World Building Through Active Learning

Model-Based Reinforcement Learning (MBRL) offers a promising direction f...

10 Philip Ball, et al. ∙

research

∙ 02/06/2020

One-Shot Bayes Opt with Probabilistic Population Based Training

Selecting optimal hyperparameters is a key challenge in machine learning...

18 Jack Parker-Holder, et al. ∙

research

∙ 02/03/2020

Effective Diversity in Population-Based Reinforcement Learning

Maintaining a population of solutions has been shown to increase explora...

18 Jack Parker-Holder, et al. ∙

research

∙ 01/14/2020

HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset

Mosquitoes are the only known vector of malaria, which leads to hundreds...

0 Ivan Kiskin, et al. ∙

research

∙ 12/20/2019

MLRG Deep Curvature

We present MLRG Deep Curvature suite, a PyTorch-based, open-source packa...

0 Diego Granziol, et al. ∙

research

∙ 12/19/2019

A Maximum Entropy approach to Massive Graph Spectra

Graph spectral techniques for measuring graph similarity, or for learnin...

20 Diego Granziol, et al. ∙

research

∙ 12/04/2019

Indian Buffet Neural Networks for Continual Learning

We place an Indian Buffet Process (IBP) prior over the neural structure ...

4 Samuel Kessler, et al. ∙

research

∙ 11/29/2019

Safety Guarantees for Planning Based on Iterative Gaussian Processes

Gaussian Processes (GPs) are widely employed in control and learning bec...

13 Kyriakos Polymenakos, et al. ∙

research

∙ 11/22/2019

Deep Reinforcement Learning for Trading

We adopt Deep Reinforcement Learning algorithms to design trading strate...

0 Zihao Zhang, et al. ∙

research

∙ 09/27/2019

Stratified Space Learning: Reconstructing Embedded Graphs

Many data-rich industries are interested in the efficient discovery and ...

0 Yossi Bokor Bleile, et al. ∙

research

∙ 09/25/2019

Regularising Deep Networks with DGMs

Here we develop a new method for regularising neural networks where we l...

10 Matthew Willetts, et al. ∙

research

∙ 09/25/2019

Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders

In clustering we normally output one cluster variable for each datapoint...

4 Matthew Willetts, et al. ∙

research

∙ 09/09/2019

Balancing Reconstruction Quality and Regularisation in ELBO for VAEs

A trade-off exists between reconstruction quality and the prior regulari...

18 Shuyu Lin, et al. ∙

research

∙ 06/03/2019

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning

Efficient approximation lies at the heart of large-scale machine learnin...

0 Diego Granziol, et al. ∙

research

∙ 06/01/2019

Disentangling Improves VAEs' Robustness to Adversarial Attacks

This paper is concerned with the robustness of VAEs to adversarial attac...

0 Matthew Willetts, et al. ∙

research

∙ 05/28/2019

Robustness Quantification for Classification with Gaussian Processes

We consider Bayesian classification with Gaussian processes (GPs) and de...

0 Arno Blaas, et al. ∙

research

∙ 05/23/2019

Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs

Despite recent innovations in network architectures and loss functions, ...

0 Bryan Lim, et al. ∙

Stephen Roberts

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