Accelerating Gradient Boosting Machines

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The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

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Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

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Various forms of real-world data, such as social, financial, and biological networks, can be represented using graphs. An efficient method of analysing this type of data is to extract subgraph patterns, such as cliques, cycles, and motifs, from graphs. For ...

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Water is a crucial resource for all forms of life, yet it is becoming increasingly scarce. A significant portion of water loss in urban and industrial areas is attributed to leaks. Addressing this issue is critical for enhancing efficiency, sustainability, ...

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While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Accelerating Gradient Boosting Machines

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Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Acceleration of graph pattern mining and applications to financial crime

Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Computational design of complex protein folds and soluble analogs of membrane proteins

Euclid: Identification of asteroid streaks in simulated images using deep learning

Classification of fall directions via wearable motion sensors

Sequential Reasoning with Socially Caused Beliefs

Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques

Understanding generalization and robustness in modern deep learning

Asymptotics of feature learning in two-layer networks after one gradient-step

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

On the Generalization of Stochastic Gradient Descent with Momentum