:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Tian, Xunye, Peng, Liuhua, Zhou, Zhijian, Gong, Mingming, Gretton, Arthur, Liu, Feng
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2412.00613
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Kernel Distribution Closeness Testing
par: Zhou, Zhijian, et autres
Publié: (2025)

Anchor-based Maximum Discrepancy for Relative Similarity Testing
par: Zhou, Zhijian, et autres
Publié: (2025)

DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
par: Zhou, Zhijian, et autres
Publié: (2025)

Perturbative methods for non-parametric instrumental variable
par: Bu, Wei, et autres
Publié: (2026)

Adapformer: Adaptive Channel Management for Multivariate Time Series Forecasting
par: Luo, Yuchen, et autres
Publié: (2025)

Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
par: Zenati, Houssam, et autres
Publié: (2026)

Credal Two-Sample Tests of Epistemic Uncertainty
par: Chau, Siu Lun, et autres
Publié: (2024)

Regularized $f$-Divergence Kernel Tests
par: Ribero, Mónica, et autres
Publié: (2026)

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

Fast and Scalable Score-Based Kernel Calibration Tests
par: Glaser, Pierre, et autres
Publié: (2025)

Kernel Single Proxy Control for Deterministic Confounding
par: Xu, Liyuan, et autres
Publié: (2023)

Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
par: Chen, Zonghao, et autres
Publié: (2025)

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
par: Xu, Liyuan, et autres
Publié: (2021)

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

Spectral Representation for Causal Estimation with Hidden Confounders
par: Sun, Haotian, et autres
Publié: (2024)

Practical Kernel Tests of Conditional Independence
par: Pogodin, Roman, et autres
Publié: (2024)

LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
par: Du, Zhekai, et autres
Publié: (2025)

Sobolev Regularized MMD Gradient Flow
par: Tian, Chenyang, et autres
Publié: (2026)

Learning U-Statistics with Active Inference
par: Wang, Xiaoning, et autres
Publié: (2026)

Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
par: Zenati, Houssam, et autres
Publié: (2025)

Spectral Souping: A Unified Framework for Online Preference Alignment
par: Chow, Yinlam, et autres
Publié: (2026)

Interventional Processes for Causal Uncertainty Quantification
par: Dance, Hugh, et autres
Publié: (2024)

Foundations of Multivariate Distributional Reinforcement Learning
par: Wiltzer, Harley, et autres
Publié: (2024)

Stable Localized Conformal Prediction via Transduction
par: Min, Yinjie, et autres
Publié: (2026)

Nonlinear Meta-Learning Can Guarantee Faster Rates
par: Meunier, Dimitri, et autres
Publié: (2023)

Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
par: Singh, Rahul, et autres
Publié: (2021)

Near-Optimality of Contrastive Divergence Algorithms
par: Glaser, Pierre, et autres
Publié: (2025)

On the Hardness of Conditional Independence Testing In Practice
par: He, Zheng, et autres
Publié: (2025)

Fine-grained Analysis of Non-parametric Estimation for Pairwise Learning
par: Zhou, Junyu, et autres
Publié: (2023)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
par: Meunier, Dimitri, et autres
Publié: (2024)

Density Ratio-Free Doubly Robust Proxy Causal Learning
par: Bozkurt, Bariscan, et autres
Publié: (2025)

Density Ratio-based Proxy Causal Learning Without Density Ratios
par: Bozkurt, Bariscan, et autres
Publié: (2025)

Deep MMD Gradient Flow without adversarial training
par: Galashov, Alexandre, et autres
Publié: (2024)

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
par: Meunier, Dimitri, et autres
Publié: (2024)

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
par: Wornbard, Jakub, et autres
Publié: (2026)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
par: Li, Zhu, et autres
Publié: (2023)

A Distributional Analogue to the Successor Representation
par: Wiltzer, Harley, et autres
Publié: (2024)

Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
par: Huang, Zhengdong, et autres
Publié: (2025)

Controlling Moments with Kernel Stein Discrepancies
par: Kanagawa, Heishiro, et autres
Publié: (2022)

Learning Representations for Independence Testing
par: Xu, Nathaniel, et autres
Publié: (2024)