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08/23/2023
Learning to Learn Financial Networks for Optimising Momentum Strategies
Network momentum provides a novel type of risk premium, which exploits t...
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08/22/2023
Network Momentum across Asset Classes
We investigate the concept of network momentum, a novel trading signal d...
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07/22/2023
The instabilities of large learning rate training: a loss landscape view
Modern neural networks are undeniably successful. Numerous works study h...
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06/24/2023
G-TRACER: Expected Sharpness Optimization
We propose a new regularization scheme for the optimization of deep lear...
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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...
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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...
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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...
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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...
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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...
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05/13/2022
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, Mosquitoes
The ACM Multimedia 2022 Computational Paralinguistics Challenge addresse...
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12/16/2021
Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture
Deep learning architectures, specifically Deep Momentum Networks (DMNs) ...
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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...
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10/21/2021
One-Shot Transfer Learning of Physics-Informed Neural Networks
Solving differential equations efficiently and accurately sits at the he...
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10/11/2021
Robust and Scalable SDE Learning: A Functional Perspective
Stochastic differential equations provide a rich class of flexible gener...
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07/16/2021
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems
Accurately learning the temporal behavior of dynamical systems requires ...
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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...
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06/04/2021
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
ResNets constrained to be bi-Lipschitz, that is, approximately distance ...
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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...
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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...
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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...
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04/07/2021
Adversarial Robustness Guarantees for Gaussian Processes
Gaussian processes (GPs) enable principled computation of model uncertai...
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12/13/2020
Building Cross-Sectional Systematic Strategies By Learning to Rank
The success of a cross-sectional systematic strategy depends critically ...
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11/15/2020
Explaining the Adaptive Generalisation Gap
We conjecture that the reason for the difference in generalisation betwe...
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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...
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07/14/2020
Explicit Regularisation in Gaussian Noise Injections
We study the regularisation induced in neural networks by Gaussian noise...
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07/14/2020
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
We make inroads into understanding the robustness of Variational Autoenc...
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07/14/2020
Relaxed-Responsibility Hierarchical Discrete VAEs
Successfully training Variational Autoencoders (VAEs) with a hierarchy o...
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06/21/2020
On Optimism in Model-Based Reinforcement Learning
The principle of optimism in the face of uncertainty is prevalent throug...
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05/27/2020
Deep Learning for Portfolio Optimisation
We adopt deep learning models to directly optimise the portfolio Sharpe ...
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04/28/2020
VIGN: Variational Integrator Graph Networks
Rich, physically-informed inductive biases play an imperative role in ac...
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04/08/2020
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
Generative Adversarial Networks (GANs) have gained significant attention...
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03/02/2020
Iterate Averaging Helps: An Alternative Perspective in Deep Learning
Iterate averaging has a rich history in optimisation, but has only very ...
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02/18/2020
Learning Bijective Feature Maps for Linear ICA
Separating high-dimensional data like images into independent latent fac...
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02/07/2020
Ready Policy One: World Building Through Active Learning
Model-Based Reinforcement Learning (MBRL) offers a promising direction f...
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02/06/2020
One-Shot Bayes Opt with Probabilistic Population Based Training
Selecting optimal hyperparameters is a key challenge in machine learning...
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02/03/2020
Effective Diversity in Population-Based Reinforcement Learning
Maintaining a population of solutions has been shown to increase explora...
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01/14/2020
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
Mosquitoes are the only known vector of malaria, which leads to hundreds...
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12/20/2019
MLRG Deep Curvature
We present MLRG Deep Curvature suite, a PyTorch-based, open-source packa...
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12/19/2019
A Maximum Entropy approach to Massive Graph Spectra
Graph spectral techniques for measuring graph similarity, or for learnin...
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12/04/2019
Indian Buffet Neural Networks for Continual Learning
We place an Indian Buffet Process (IBP) prior over the neural structure ...
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11/29/2019
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Gaussian Processes (GPs) are widely employed in control and learning bec...
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11/22/2019
Deep Reinforcement Learning for Trading
We adopt Deep Reinforcement Learning algorithms to design trading strate...
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09/27/2019
Stratified Space Learning: Reconstructing Embedded Graphs
Many data-rich industries are interested in the efficient discovery and ...
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09/25/2019
Regularising Deep Networks with DGMs
Here we develop a new method for regularising neural networks where we l...
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09/25/2019
Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders
In clustering we normally output one cluster variable for each datapoint...
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09/09/2019
Balancing Reconstruction Quality and Regularisation in ELBO for VAEs
A trade-off exists between reconstruction quality and the prior regulari...
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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...
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06/01/2019
Disentangling Improves VAEs' Robustness to Adversarial Attacks
This paper is concerned with the robustness of VAEs to adversarial attac...
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05/28/2019
Robustness Quantification for Classification with Gaussian Processes
We consider Bayesian classification with Gaussian processes (GPs) and de...
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05/23/2019