:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Bolgár, Bence, Millinghoffer, András, Antal, Péter
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2512.24810
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Characterization of Transfer Using Multi-task Learning Curves
by: Millinghoffer, András, et al.
Published: (2025)

Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learning
by: Antos, András, et al.
Published: (2025)

BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Framework
by: Millinghoffer, András, et al.
Published: (2025)

GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction
by: Jiang, Feng, et al.
Published: (2025)

MKDTI: Predicting drug-target interactions via multiple kernel fusion on graph attention network
by: Zhou, Yuhuan, et al.
Published: (2024)

Bayesian autoregression to optimize temporal Matérn kernel Gaussian process hyperparameters
by: Kouw, Wouter M.
Published: (2025)

Unified Causality Analysis Based on the Degrees of Freedom
by: Telcs, András, et al.
Published: (2024)

Heterogeneous networks in drug-target interaction prediction
by: Molaee, Mohammad, et al.
Published: (2025)

A large dataset curation and benchmark for drug target interaction
by: Golts, Alex, et al.
Published: (2024)

Gradient-enhanced deep Gaussian processes for multifidelity modelling
by: Bone, Viv, et al.
Published: (2024)

A mixed-categorical correlation kernel for Gaussian process
by: Saves, P., et al.
Published: (2022)

The BdryMatérn GP: Reliable incorporation of boundary information on irregular domains for Gaussian process modeling
by: Ding, Liang, et al.
Published: (2025)

dynoGP: Deep Gaussian Processes for dynamic system identification
by: Benavoli, Alessio, et al.
Published: (2025)

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
by: Feng, Xiaodong, et al.
Published: (2025)

Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression
by: Ahmed, Md. Imtyaz, et al.
Published: (2025)

Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
by: Perez, Raphaël Carpintero, et al.
Published: (2024)

A reproducible comparative study of categorical kernels for Gaussian process regression, with new clustering-based nested kernels
by: Perez, Raphaël Carpintero, et al.
Published: (2025)

HiGP: A high-performance Python package for Gaussian Process
by: Huang, Hua, et al.
Published: (2025)

Enhancing pretraining efficiency for medical image segmentation via transferability metrics
by: Hidy, Gábor, et al.
Published: (2024)

GP+: A Python Library for Kernel-based learning via Gaussian Processes
by: Yousefpour, Amin, et al.
Published: (2023)

SaeGraphDTI: drug-target interaction prediction based on sequence attribute extraction and graph neural network.
by: Zhang, Qiaosheng, et al.
Published: (2025)

EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems
by: Chen, Jianhong, et al.
Published: (2025)

On the Gaussian process limit of Bayesian Additive Regression Trees
by: Petrillo, Giacomo
Published: (2024)

Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment
by: Bagheri, Abdolmahdi, et al.
Published: (2023)

DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process
by: Abu-Shaira, Mohammad, et al.
Published: (2025)

Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
by: Risser, Mark D., et al.
Published: (2024)

Mesh motion in fluid-structure interaction with deep operator networks
by: Hellan, Ottar
Published: (2024)

Deep graph kernel point processes
by: Dong, Zheng, et al.
Published: (2023)

RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy
by: Kraus, Oren, et al.
Published: (2025)

Learning functions, operators and dynamical systems with kernels
by: Rosasco, Lorenzo
Published: (2025)

Circuits, Features, and Heuristics in Molecular Transformers
by: Varadi, Kristof, et al.
Published: (2025)

Shared Keyboard: An improved Bayesian design for phase I clinical trials via Beta kernel process
by: Zhao, Jiangyan, et al.
Published: (2026)

Uniform convergence for Gaussian kernel ridge regression
by: Dommel, Paul, et al.
Published: (2025)

Surrogate modeling for Bayesian optimization beyond a single Gaussian process
by: Lu, Qin, et al.
Published: (2022)

Inferring manifolds using Gaussian processes
by: Dunson, David B, et al.
Published: (2021)

Taking the GP Out of the Loop
by: Bafna, Mehul, et al.
Published: (2025)

Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
by: Bai, Tianming, et al.
Published: (2023)

Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes
by: Shen, Zhenqian, et al.
Published: (2024)

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators
by: Borrel-Jensen, Nikolas, et al.
Published: (2023)

On the number of modes of Gaussian kernel density estimators
by: Geshkovski, Borjan, et al.
Published: (2024)