Publications related to Generalization error rates in kernel regression: the crossover from the noiseless to noisy regime*

Related publications (20)

Branching fraction measurement of the decay B+→ψ(2S)ϕ(1020)K+

Olivier Schneider, Aurelio Bay, Guido Haefeli, Tatsuya Nakada, Frédéric Blanc, Lesya Shchutska, Elena Graverini, Sebastian Schulte, Marie Theres Christin Bachmayer, Serhii Cholak, Ettore Zaffaroni, Aravindhan Venkateswaran, Luis Miguel Garcia Martin, Yunxuan Song, Vitalii Lisovskyi, Sonia Amina Bouchiba, Federico Ronchetti, Radoslav Marchevski, Anni Matilda Kauniskangas, Dimitrios Kaminaris, Raphaël van Laak, Pierre Paul Louis Mayencourt, Andrea Merli, Gianluca Zunica, Abdul-Kerim Guseinov, Roberto Ribatti, Spencer Edward Collaviti

The branching fraction of the decay B + → ψ ( 2 S ) ϕ ( 1020 ) K + , relative to the topologically similar decay B + → J / ψ ϕ ( 1020 ) K + , is measured using proton-proton collision data collected by the LHCb experiment at center-of-mass energies of 7, 8 ...

American Physical Society (APS)2025

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Ramin Mohammadi, Nikolaos Stergiopoulos, Georgios Rovas, Lydia Aslanidou, Sokratis Anagnostopoulos

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

2025

On the added value of sequential deep learning for the upscaling of evapotranspiration

Devis Tuia

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

2025

Search for the Double Dalitz Decays η/η' →e + e -μ +μ - and η'→μ+μ-μ+μ-

Yunxuan Song

Using a data sample of (10087 +/- 44)x10(6) J/psi events collected with the BESIII detector, we search for the decays eta/eta' -> e (+) e (-)mu (+)mu (-) and eta'->mu(+)mu(-)mu(+)mu(-) via the radiative decaysJ/psi ->gamma eta/gamma eta '. No excess of eve ...

AMER PHYSICAL SOC2025

Study of light-meson resonances decaying to K S0 Kπ in the B → (K S0 Kπ)K channels

Olivier Schneider, Aurelio Bay, Guido Haefeli, Tatsuya Nakada, Frédéric Blanc, Lesya Shchutska, Elena Graverini, Sebastian Schulte, Marie Theres Christin Bachmayer, Ettore Zaffaroni, Aravindhan Venkateswaran, Luis Miguel Garcia Martin, Yunxuan Song, Vitalii Lisovskyi, Sonia Amina Bouchiba, Federico Ronchetti, Radoslav Marchevski, Anni Matilda Kauniskangas, Dimitrios Kaminaris, Raphaël van Laak, Pierre Paul Louis Mayencourt, Andrea Merli, Gianluca Zunica, Abdul-Kerim Guseinov, Roberto Ribatti, Spencer Edward Collaviti

A study is presented of B+→KS0K-π+K+ and B+→KS0K+π-K+ decays based on the analysis of proton-proton collision data collected with the LHCb detector at center-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of 9 fb-1. The KS0K ...

2025

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Zaffar Ahmed Shaikh

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

PeerJ2025

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Su Yeon Chang

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

IOP Publishing Ltd2025

Inverse Geometric Locomotion

Mark Pauly, Quentin Christian Becker

Numerous tasks in robotics and character animation involve solving combinations of inverse kinematics and motion planning problems that require the precise design of pose sequences to achieve desired motion objectives. Accounting for the complex interplay ...

2025

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Alfio Quarteroni

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

2025

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Jan Weinreich, Matthieu Nicolas Haeberle

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

IOP Publishing Ltd2025

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Michael Lehning, Devis Tuia, Yael Frischholz

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

2025

Rosette Spectroscopic Imaging for Whole-Brain Slab Metabolite Mapping at 7T: Acceleration Potential and Reproducibility

Lijing Xin, Mark Stephan Widmaier, Ying Xiao, Zhiwei Huang, André Döring

Whole-brain proton magnetic resonance spectroscopic imaging (H-1-MRSI) is a non-invasive technique for assessing neurochemical distribution in the brain, offering valuable insights into brain functions and neural diseases. It greatly benefits from the impr ...

Wiley2025

Neural Garment Modeling

Ren Li

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

EPFL2025

Introduction and Overview: Chapter 1 of the Special Issue: on the Path to Tokamak Burning Plasma Operation

Ambrogio Fasoli

The International Tokamak Physics Activity (ITPA) has developed a comprehensive overview of the results of its coordinated R&D activities in fusion physics and diagnostics implemented during the past two decades with the aim of developing an improved physi ...

IOP Publishing Ltd2025

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Jérôme Chenal, Baraka Jean-Claude Munyaka

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

AIP Publishing2025

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Zimin Xia

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on dev ...

Springer Science and Business Media Deutschland GmbH2025

Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things

Olga Fink, Keivan Faghih Niresi

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intell ...

2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Replica method for computational problems with randomness: principles and illustrations

Damien Yves Valery Barbier, Pierre Mergny

2024

Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns

Lei Xu

Fiber reinforced polymer confined-recycled aggregate concrete filled steel tube (FRP-RACFST) not only proposes excellent load-bearing capacity and corrosion resistance, but is also an environmentally friendly structure due to the incorporation of recycled ...

ELSEVIER2024