Ensemble learning for portfolio valuation and risk management

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The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor dr ...

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

IntroductionRecent efforts have been made to apply machine learning and deep learning approaches to the automated classification of schizophrenia using structural magnetic resonance imaging (sMRI) at the individual level. However, these approaches are less ...

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and effici ...

The shear resistance of headed studs is of paramount importance for the design of steel-concrete composite structures and an accurate predictive model is highly needed. Ensemble learning is expected to be a powerful solution while it relies on laborious se ...

Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hi ...

In this paper we investigate how gradient-based algorithms such as gradient descent (GD), (multi-pass) stochastic GD, its persistent variant, and the Langevin algorithm navigate non-convex loss-landscapes and which of them is able to reach the best general ...

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

Transition to low-carbon power systems is in progress, fuelled by policy-driven accelerated deployment of new renewable electricity generation technologies (wind and solar), smart electrification, prosumerisation and digitisation. Largely occurring against ...

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomi ...

The paper introduces a functional time series (lagged) regression model. The impulse-response coefficients in such a model are operators acting on a separable Hilbert space, which is the function space L-2 in applications. A spectral approach to the estima ...

A common trend in machine learning and pattern classification research is the exploitation of massive amounts of information in order to achieve an increase in performance. In particular, learning from huge collections of data obtained from the web, and us ...

Ensemble learning for portfolio valuation and risk management

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Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion

TimeGPT in load forecasting: A large time series model perspective

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Topics in statistical physics of high-dimensional machine learning

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Bridging structural MRI with cognitive function for individual level classification of early psychosis via deep learning

Machine-learning accelerated identification of exfoliable two-dimensional materials

Auto-tuning ensemble models for estimating shear resistance of headed studs in concrete

Learning physics-consistent particle interactions

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Cognitive workload monitoring in virtual reality based rescue missions with drones

Robust Generalization via $\alpha$-Mutual Information

The Vanishing Hand: Efficient Market Design for Future Power Systems

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies

Estimation in Functional Lagged Regression

Open-Ended Learning of Visual and Multi-Modal Patterns