Copertina

When Marginals Match but Structure Fails: Covariance Fidelity in Generative Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Riasat, Nazia
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence Methodology 62H20, 62J05, 15A18, 62G99, 68T05, 60E05 I.2.6; G.3; G.1.3; I.5.1
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations
di: Maturo, Fabrizio, et al.
Pubblicazione: (2024)

Throwing Vines at the Wall: Structure Learning via Random Search
di: Vatter, Thibault, et al.
Pubblicazione: (2025)

Scalable Bayesian Clustering for Integrative Analysis of Multi-View Data
di: Cabral, Rafael, et al.
Pubblicazione: (2024)

SplitWise Regression: Stepwise Modeling with Adaptive Dummy Encoding
di: Kurbucz, Marcell T., et al.
Pubblicazione: (2025)

DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learning
di: Tirumala, Aditya Puttaparthi
Pubblicazione: (2025)

Bayesian Modal Regression for Forecast Combinations
di: van Eijk, Henry D., et al.
Pubblicazione: (2025)

Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests
di: Loffredo, Giuseppe, et al.
Pubblicazione: (2025)

When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression
di: Kurbucz, Marcell T.
Pubblicazione: (2026)

Demystifying Functional Random Forests: Novel Explainability Tools for Model Transparency in High-Dimensional Spaces
di: Maturo, Fabrizio, et al.
Pubblicazione: (2024)

From Point to probabilistic gradient boosting for claim frequency and severity prediction
di: Chevalier, Dominik, et al.
Pubblicazione: (2024)

Physics-informed machine learning: A mathematical framework with applications to time series forecasting
di: Doumèche, Nathan
Pubblicazione: (2025)

Iterative Exploration-Driven Sparse SDP Clustering via Thompson Sampling
di: Mun, Jongmin, et al.
Pubblicazione: (2025)

TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery
di: Ferdous, Muhammad Hasan, et al.
Pubblicazione: (2025)

Lightweight Defense Against Adversarial Attacks in Time Series Classification
di: Han, Yi
Pubblicazione: (2025)

Intra-tree Column Subsampling Hinders XGBoost Learning of Ratio-like Interactions
di: Pinchuk, Mykola
Pubblicazione: (2026)

Unifying small area estimators based on area-level and unit-level models through calibration
di: Acero, William, et al.
Pubblicazione: (2024)

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization
di: Raichur, Nisha Lakshmana, et al.
Pubblicazione: (2025)

Boxplots and quartile plots for grouped and periodic angular data
di: Berlinski, Joshua D., et al.
Pubblicazione: (2026)

Leveraging Machine Learning for Official Statistics: A Statistical Manifesto
di: Puts, Marco, et al.
Pubblicazione: (2024)

Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects
di: Liu, Yichen
Pubblicazione: (2025)

A Bayesian Updating Framework for Long-term Multi-Environment Trial Data in Plant Breeding
di: Bark, Stephan, et al.
Pubblicazione: (2026)

Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
di: Høyheim, Eirik, et al.
Pubblicazione: (2025)

Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
di: Hubin, Aliaksandr
Pubblicazione: (2025)

FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
di: Frommlet, Florian, et al.
Pubblicazione: (2025)

Classifier Calibration at Scale: An Empirical Study of Model-Agnostic Post-Hoc Methods
di: Manokhin, Valery, et al.
Pubblicazione: (2026)

Calibrated Counterfactual Conformal Fairness ($C^3F$): Post-hoc, Shift-Aware Coverage Parity via Conformal Prediction and Counterfactual Regularization
di: Alpay, Faruk, et al.
Pubblicazione: (2025)

Ranking of Multi-Response Experiment Treatments
di: Pebes-Trujillo, Miguel R., et al.
Pubblicazione: (2024)

Nonparametric Estimation via Expected Order Statistics
di: Lando, Tommaso, et al.
Pubblicazione: (2026)

Sparse joint shift in multinomial classification
di: Tasche, Dirk
Pubblicazione: (2023)

Can we spot a fake?
di: Mendelson, Shahar, et al.
Pubblicazione: (2024)

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
di: Galvão, Luciano Ribeiro, et al.
Pubblicazione: (2025)

Insights into Tail-Based and Order Statistics
di: Almani, Hamidreza Maleki
Pubblicazione: (2025)

Robust X-Learner: Breaking the Curse of Imbalance and Heavy Tails via Robust Cross-Imputation
di: Uehara, Eichi
Pubblicazione: (2026)

Copula Analysis of Risk: A Multivariate Risk Analysis for VaR and CoVaR using Copulas and DCC-GARCH
di: Singh, Aryan, et al.
Pubblicazione: (2025)

Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers
di: Tuononen, Marko, et al.
Pubblicazione: (2025)

Geometric Mixture Classifier (GMC): A Discriminative Per-Class Mixture of Hyperplanes
di: K, Prasanth K, et al.
Pubblicazione: (2025)

Quantile Additive Trend Filtering
di: Zhang, Zhi, et al.
Pubblicazione: (2023)

Variance-Reduced Diffusion Sampling via Target Score Identity
di: Duston, Alois, et al.
Pubblicazione: (2026)

Optimization-Free Topological Sort for Causal Discovery via the Schur Complement of Score Jacobians
di: Wu, Rui, et al.
Pubblicazione: (2026)

A Fiber Criterion for Representation Identifiability in Supervised Learning
di: Sevetlidis, Vasileios
Pubblicazione: (2026)