Image de couverture de livre

Calibrating dimension reduction hyperparameters in the presence of noise

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lin, Justin, Fukuyama, Julia
Format:	Preprint
Publié:	2023
Sujets:	Applications Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

DRtool: An Interactive Tool for Analyzing High-Dimensional Clusterings
par: Lin, Justin, et autres
Publié: (2025)

On metric choice in dimension reduction for Fréchet regression
par: Soale, Abdul-Nasah, et autres
Publié: (2024)

Revisiting PCA for time series reduction in temporal dimension
par: Gao, Jiaxin, et autres
Publié: (2024)

Fault detection in propulsion motors in the presence of concept drift
par: Tveten, Martin, et autres
Publié: (2024)

Variance reduction combining pre-experiment and in-experiment data
par: Lin, Zhexiao, et autres
Publié: (2024)

Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs
par: Davidov, Hen, et autres
Publié: (2025)

A comparative analysis of machine learning models in SHAP analysis
par: Lin, Justin, et autres
Publié: (2026)

Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands
par: Tassopoulou, Vasiliki, et autres
Publié: (2025)

Input layer regularization and automated regularization hyperparameter tuning for myelin water estimation using deep learning
par: Modi, Mirage, et autres
Publié: (2025)

Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression
par: García-Ruiz, Rubén Antonio, et autres
Publié: (2024)

Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification
par: Kim, Jungho, et autres
Publié: (2024)

SHAP-Based Supervised Clustering for Sample Classification and the Generalized Waterfall Plot
par: Lin, Justin, et autres
Publié: (2025)

Effective climate policies for major emission reductions of ozone precursors: Global evidence from two decades
par: Yao, Ningning, et autres
Publié: (2025)

BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
par: Sharpnack, James, et autres
Publié: (2024)

Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort
par: Chen, Shumeng, et autres
Publié: (2026)

Data-driven Calibration Sample Selection and Forecast Combination in Electricity Price Forecasting: An Application of the ARHNN Method
par: Serafin, Tomasz, et autres
Publié: (2025)

Causal Judge Evaluation: Calibrated Surrogate Metrics for LLM Systems
par: Landesberg, Eddie, et autres
Publié: (2025)

Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction
par: Stroh, Rémi, et autres
Publié: (2020)

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
par: Saxena, Manan, et autres
Publié: (2024)

An early warning indicator trained on stochastic disease-spreading models with different noises
par: Chakraborty, Amit K., et autres
Publié: (2024)

Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
par: Giovannelli, Jean-François
Publié: (2026)

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations
par: Murph, Alexander C., et autres
Publié: (2023)

Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques
par: Park, Yoon Tae, et autres
Publié: (2024)

Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions
par: Lin, Gefei, et autres
Publié: (2026)

Ranking and Combining Latent Structured Predictive Scores without Labeled Data
par: Afshar, Shiva, et autres
Publié: (2024)

AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
par: Sharpnack, James, et autres
Publié: (2024)

Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis
par: Sun, Jingyao, et autres
Publié: (2025)

Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects
par: Wang, Hao, et autres
Publié: (2026)

Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems
par: Simeone, Osvaldo, et autres
Publié: (2025)

Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data
par: Zhang, Xinyu, et autres
Publié: (2026)

Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
par: Kosko, Matthew, et autres
Publié: (2026)

Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties
par: Riccius, Leon, et autres
Publié: (2024)

Detection of Interacting Variables for Generalized Linear Models via Neural Networks
par: Havrylenko, Yevhen, et autres
Publié: (2022)

Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning
par: Zhou, Cai, et autres
Publié: (2026)

An Empirical Study: Extensive Deep Temporal Point Process
par: Lin, Haitao, et autres
Publié: (2021)

A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values
par: Caruso, Camillo Maria, et autres
Publié: (2023)

Ellipsoid fitting with the Cayley transform
par: Melikechi, Omar, et autres
Publié: (2023)

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering
par: Valleggi, Lorenzo, et autres
Publié: (2023)

Variable Selection for Kernel Two-Sample Tests
par: Wang, Jie, et autres
Publié: (2023)

Meta-models for transfer learning in source localisation
par: Bull, Lawrence A., et autres
Publié: (2023)