Image de couverture de livre

A general framework for adaptive nonparametric dimensionality reduction

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Di Noia, Antonio, Ravenda, Federico, Mira, Antonietta
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Scale-adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification
par: Di Noia, Antonio, et autres
Publié: (2024)

Likelihood distortion and Bayesian local robustness
par: Di Noia, Antonio, et autres
Publié: (2024)

Fairboard: a quantitative framework for equity assessment of healthcare models
par: Ruffle, James K., et autres
Publié: (2026)

Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice
par: Ravenda, Federico, et autres
Publié: (2025)

High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates
par: Weberpals, Janick, et autres
Publié: (2024)

Navigating through the hidden embedding space: steering LLMs to improve mental health assessment
par: Ravenda, Federico, et autres
Publié: (2025)

Asymptotic theory for nonparametric testing of $k$-monotonicity in discrete distributions
par: Balabdaoui, Fadoua, et autres
Publié: (2024)

Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
par: Hassan, Conor, et autres
Publié: (2024)

Adversarial learning for nonparametric regression: Minimax rate and adaptive estimation
par: Peng, Jingfu, et autres
Publié: (2025)

A Causal Framework for Evaluating ICU Discharge Strategies
par: Simha, Sagar Nagaraj, et autres
Publié: (2026)

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
par: Rahman, Md Musfiqur, et autres
Publié: (2024)

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
par: James, Zachary, et autres
Publié: (2025)

Is Elo Rating Reliable? A Study Under Model Misspecification
par: Tang, Shange, et autres
Publié: (2025)

A Recipe for Causal Graph Regression: Confounding Effects Revisited
par: Yin, Yujia, et autres
Publié: (2025)

A Consequentialist Critique of Binary Classification Evaluation: Theory, Practice, and Tools
par: Flores, Gerardo, et autres
Publié: (2025)

Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
par: Wienöbst, Marcel, et autres
Publié: (2025)

A Generalized Similarity U Test for Multivariate Analysis of Sequencing Data
par: Wei, Changshuai, et autres
Publié: (2015)

A General Causal Inference Framework for Cross-Sectional Observational Data
par: Zhao, Yonghe, et autres
Publié: (2024)

A weighted U statistic for association analysis considering genetic heterogeneity
par: Wei, Changshuai, et autres
Publié: (2015)

A Critical Perspective on Finite Sample Conformal Prediction Theory in Medical Applications
par: Kladny, Klaus-Rudolf, et autres
Publié: (2025)

First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
par: Liu, Ziqi, et autres
Publié: (2026)

Confounder Detection via Treatment Intent: A New Observational Study Design
par: Plecko, Drago, et autres
Publié: (2026)

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
par: Takayama, Masayuki, et autres
Publié: (2024)

A multi-locus predictiveness curve and its summary assessment for genetic risk prediction
par: Wei, Changshuai, et autres
Publié: (2025)

A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
par: Li, Ming, et autres
Publié: (2025)

A feature selection method based on Shapley values robust to concept shift in regression
par: Sebastián, Carlos, et autres
Publié: (2023)

Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
par: Li, Jiangmeng, et autres
Publié: (2024)

Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset
par: Shukla, Manish
Publié: (2025)

Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation
par: Yang, Tianyu, et autres
Publié: (2025)

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series
par: Ma, Zhipeng, et autres
Publié: (2024)

Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric
par: Skat-Rørdam, Harald Vilhelm, et autres
Publié: (2025)

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
par: Liu, Xiaohui, et autres
Publié: (2022)

TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
par: González, Javier, et autres
Publié: (2023)

A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data
par: Islam, Md Saiful, et autres
Publié: (2021)

Best of mini-N in-loop Sampling: A Contextual Quality Reward Model for Reliable and Efficient Best-of-N Sampling
par: Rho, Hyung Gyu, et autres
Publié: (2025)

Identifying Conditional Causal Effects in MPDAGs
par: LaPlante, Sara, et autres
Publié: (2025)

Statistical Methods in Generative AI
par: Dobriban, Edgar
Publié: (2025)

PCS Workflow for Veridical Data Science in the Age of AI
par: Rewolinski, Zachary T., et autres
Publié: (2025)

TRUST: Transparent, Robust and Ultra-Sparse Trees
par: Dorador, Albert
Publié: (2025)

On the Granularity of Causal Effect Identifiability
par: Chen, Yizuo, et autres
Publié: (2025)