:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Thanasutives, Pongpisit, Ke, Naichang, Kawahara, Yoshinobu
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Applications Machine Learning
Accesso online:	https://arxiv.org/abs/2605.26631
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators
di: Ke, Naichang, et al.
Pubblicazione: (2025)

On uncertainty-penalized Bayesian information criterion
di: Thanasutives, Pongpisit, et al.
Pubblicazione: (2024)

Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
di: Thanasutives, Pongpisit, et al.
Pubblicazione: (2024)

Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery
di: Thanasutives, Pongpisit, et al.
Pubblicazione: (2023)

Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
di: Grapin, Robin, et al.
Pubblicazione: (2025)

Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification
di: Hermansen, Tobias Østmo, et al.
Pubblicazione: (2025)

Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis
di: Sun, Jingyao, et al.
Pubblicazione: (2025)

Modelling higher education dropouts using sparse and interpretable post-clustering logistic regression
di: Nigri, Andrea, et al.
Pubblicazione: (2025)

Data-driven Calibration Sample Selection and Forecast Combination in Electricity Price Forecasting: An Application of the ARHNN Method
di: Serafin, Tomasz, et al.
Pubblicazione: (2025)

Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain
di: Naumzik, Christof, et al.
Pubblicazione: (2024)

Ensemble Kalman filter for uncertainty in human language comprehension
di: Bhandari, Diksha, et al.
Pubblicazione: (2025)

Multivariate Time Series Data Imputation via Distributionally Robust Regularization
di: Liao, Che-Yi, et al.
Pubblicazione: (2026)

Supervised Multiple Kernel Learning approaches for multi-omics data integration
di: Briscik, Mitja, et al.
Pubblicazione: (2024)

Gotta match 'em all: Solution diversification in graph matching matched filters
di: Li, Zhirui, et al.
Pubblicazione: (2023)

Pandemic model with data-driven phase detection, a study using COVID-19 data
di: Liu, Yuansan, et al.
Pubblicazione: (2021)

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering
di: Valleggi, Lorenzo, et al.
Pubblicazione: (2023)

Nonlinear Principal Component Analysis with Random Bernoulli Features for Process Monitoring
di: Chen, Ke, et al.
Pubblicazione: (2025)

Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
di: Zhou, Biying, et al.
Pubblicazione: (2025)

Adapting tree-based multiple imputation methods for multi-level data? A simulation study
di: Föge, Nico, et al.
Pubblicazione: (2024)

Bayesian sparse modeling for interpretable prediction of hydroxide ion conductivity in anion-conductive polymer membranes
di: Murakami, Ryo, et al.
Pubblicazione: (2025)

A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records
di: Mota, Marco Barbero, et al.
Pubblicazione: (2025)

Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem
di: Esteghamati, Mohsen Zaker
Pubblicazione: (2025)

Interval Prediction of Annual Average Daily Traffic on Local Roads via Quantile Random Forest with High-Dimensional Spatial Data
di: Yao, Ying, et al.
Pubblicazione: (2025)

Bayesian temporal biclustering with applications to multi-subject neuroscience studies
di: Ricci, Federica Zoe, et al.
Pubblicazione: (2024)

A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks
di: Kays, H M Imran, et al.
Pubblicazione: (2024)

Second-order group knockoffs with applications to GWAS
di: Chu, Benjamin B, et al.
Pubblicazione: (2023)

Network knockoffs: controlling false discovery in dyadic space
di: Van Ee, Justin, et al.
Pubblicazione: (2026)

Predictive Hotspot Mapping for Data-driven Crime Prediction
di: Sriram, Karthik, et al.
Pubblicazione: (2026)

Missing Data Imputation With Granular Semantics and AI-driven Pipeline for Bankruptcy Prediction
di: Chakraborty, Debarati, et al.
Pubblicazione: (2024)

Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction
di: Stroh, Rémi, et al.
Pubblicazione: (2020)

Multi-task Modeling for Engineering Applications with Sparse Data
di: Comlek, Yigitcan, et al.
Pubblicazione: (2026)

Starting Off on the Wrong Foot: Pitfalls in Data Preparation
di: Guo, Jiayi, et al.
Pubblicazione: (2026)

Monitoring Machine Learning Forecasts for Platform Data Streams
di: Rombouts, Jeroen, et al.
Pubblicazione: (2024)

Graph Data Modeling: Molecules, Proteins, & Chemical Processes
di: Barraza-Chavez, José Manuel, et al.
Pubblicazione: (2025)

Context-Aware Automated Passenger Counting Data Denoising
di: Cherrier, Noëlie, et al.
Pubblicazione: (2024)

Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints
di: Raihan, Ahmed Shoyeb, et al.
Pubblicazione: (2025)

From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk
di: Ma, Chenzhi, et al.
Pubblicazione: (2025)

Variational Approximated Restricted Maximum Likelihood Estimation for Spatial Data
di: Thakur, Debjoy
Pubblicazione: (2026)

Modeling Information Blackouts in Missing Not-At-Random Time Series Data
di: Sunesh, Aman, et al.
Pubblicazione: (2026)

Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
di: Johnson, J. Emmanuel, et al.
Pubblicazione: (2020)