:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Graziani, Carlo, Ngom, Marieme
Format:	Preprint
Publié:	2025
Sujets:	Methodology Machine Learning
Accès en ligne:	https://arxiv.org/abs/2509.15244
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Targeted Adaptive Design
par: Graziani, Carlo, et autres
Publié: (2022)

The Why and How of Convex Clustering
par: Chi, Eric C., et autres
Publié: (2025)

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
par: Blöbaum, Patrick, et autres
Publié: (2022)

Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why?
par: Andrew, Bailey, et autres
Publié: (2026)

Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents
par: Liu, Xiaotong, et autres
Publié: (2026)

Extending $μ$P: Spectral Conditions for Feature Learning Across Optimizers
par: Gupta, Akshita, et autres
Publié: (2026)

When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systems
par: Cho, Young Hyun, et autres
Publié: (2026)

Universal Modelling of Autocovariance Functions via Spline Kernels
par: Astfalck, Lachlan
Publié: (2025)

Kernel Tests of Equivalence
par: Liu, Xing, et autres
Publié: (2026)

A Set of Rules for Model Validation
par: Camacho, José
Publié: (2025)

The Kernel Manifold: A Geometric Approach to Gaussian Process Model Selection
par: Islam, Md Shafiqul, et autres
Publié: (2026)

The Generalised Kernel Covariance Measure
par: Bergen, Luca, et autres
Publié: (2026)

Transfer Learning for Kernel-based Regression
par: Wang, Chao, et autres
Publié: (2023)

Composite Goodness-of-fit Tests with Kernels
par: Key, Oscar, et autres
Publié: (2021)

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
par: Matabuena, Marcos, et autres
Publié: (2025)

Robust Local Polynomial Regression with Similarity Kernels
par: Shulman, Yaniv
Publié: (2025)

Transfer Learning of CATE with Kernel Ridge Regression
par: Kim, Seok-Jin, et autres
Publié: (2025)

Kernel Treatment Effects with Adaptively Collected Data
par: Zenati, Houssam, et autres
Publié: (2025)

Adaptive Kernel Density Estimation with Pre-training
par: Zhang, Ruitong, et autres
Publié: (2026)

Recursive Estimation of Conditional Kernel Mean Embeddings
par: Tamás, Ambrus, et autres
Publié: (2023)

An Overview of Causal Inference using Kernel Embeddings
par: Sejdinovic, Dino
Publié: (2024)

Comparing Foundation Models using Data Kernels
par: Duderstadt, Brandon, et autres
Publié: (2023)

Metric Graph Kernels via the Tropical Torelli Map
par: Cao, Yueqi, et autres
Publié: (2025)

Randomized Quasi-Monte Carlo Features for Kernel Approximation
par: Huang, Yian, et autres
Publié: (2025)

A Unified View of Optimal Kernel Hypothesis Testing
par: Schrab, Antonin
Publié: (2025)

A Semi-Supervised Kernel Two-Sample Test
par: Lee, Gyumin, et autres
Publié: (2026)

Adaptive Experimentation When You Can't Experiment
par: Zhao, Yao, et autres
Publié: (2024)

A Randomized Permutation Whole-Model Test Heuristic for Self-Validated Ensemble Models (SVEM)
par: Karl, Andrew T.
Publié: (2024)

SpeedCP: Fast Kernel-based Conditional Conformal Prediction
par: Liu, Yating, et autres
Publié: (2025)

Optimal Kernel Choice for Score Function-based Causal Discovery
par: Wang, Wenjie, et autres
Publié: (2024)

Changing the Kernel During Training Leads to Double Descent in Kernel Regression
par: Allerbo, Oskar
Publié: (2023)

LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information
par: van Buuren, Stef
Publié: (2026)

Generalized Kernel Thinning
par: Dwivedi, Raaz, et autres
Publié: (2021)

Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning
par: Tanaka, Masahiro
Publié: (2025)

Kernel-Based Ensemble Gaussian Mixture Probability Hypothesis Density Filter
par: Durant, Dalton, et autres
Publié: (2025)

DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
par: Wu, Dongze, et autres
Publié: (2025)

Detecting Changes in Causal Dependence with Kernels and Copulas
par: Gavioli-Akilagun, Shakeel, et autres
Publié: (2026)

Causal Inference Isn't Special: Why It's Just Another Prediction Problem
par: Fernández-Loría, Carlos
Publié: (2025)

Valid Inference After Causal Discovery
par: Gradu, Paula, et autres
Publié: (2022)

Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
par: Zou, Yating, et autres
Publié: (2025)