Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | Meunier, Dimitri, Shen, Zikai, Mollenhauer, Mattes, Gretton, Arthur, Li, Zhu |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
by: Li, Zhu, et al.
Published: (2023)
by: Li, Zhu, et al.
Published: (2023)
Regularized least squares learning with heavy-tailed noise is minimax optimal
by: Mollenhauer, Mattes, et al.
Published: (2025)
by: Mollenhauer, Mattes, et al.
Published: (2025)
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
by: Wornbard, Jakub, et al.
Published: (2026)
by: Wornbard, Jakub, et al.
Published: (2026)
Nonlinear Meta-Learning Can Guarantee Faster Rates
by: Meunier, Dimitri, et al.
Published: (2023)
by: Meunier, Dimitri, et al.
Published: (2023)
Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
by: Meunier, Dimitri, et al.
Published: (2024)
by: Meunier, Dimitri, et al.
Published: (2024)
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
by: Bozkurt, Bariscan, et al.
Published: (2026)
by: Bozkurt, Bariscan, et al.
Published: (2026)
Nonparametric Instrumental Variable Regression with Observed Covariates
by: Shen, Zikai, et al.
Published: (2025)
by: Shen, Zikai, et al.
Published: (2025)
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
by: Kim, Juno, et al.
Published: (2025)
by: Kim, Juno, et al.
Published: (2025)
Demystifying Spectral Feature Learning for Instrumental Variable Regression
by: Meunier, Dimitri, et al.
Published: (2025)
by: Meunier, Dimitri, et al.
Published: (2025)
Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
by: Shen, Zikai, et al.
Published: (2026)
by: Shen, Zikai, et al.
Published: (2026)
Density Ratio-Free Doubly Robust Proxy Causal Learning
by: Bozkurt, Bariscan, et al.
Published: (2025)
by: Bozkurt, Bariscan, et al.
Published: (2025)
Density Ratio-based Proxy Causal Learning Without Density Ratios
by: Bozkurt, Bariscan, et al.
Published: (2025)
by: Bozkurt, Bariscan, et al.
Published: (2025)
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
by: Meunier, Dimitri, et al.
Published: (2025)
by: Meunier, Dimitri, et al.
Published: (2025)
Near-Optimality of Contrastive Divergence Algorithms
by: Glaser, Pierre, et al.
Published: (2025)
by: Glaser, Pierre, et al.
Published: (2025)
Regularized $f$-Divergence Kernel Tests
by: Ribero, Mónica, et al.
Published: (2026)
by: Ribero, Mónica, et al.
Published: (2026)
Sobolev Regularized MMD Gradient Flow
by: Tian, Chenyang, et al.
Published: (2026)
by: Tian, Chenyang, et al.
Published: (2026)
Efficient Inference after Directionally Stable Adaptive Experiments
by: Shen, Zikai, et al.
Published: (2026)
by: Shen, Zikai, et al.
Published: (2026)
Kernel Single Proxy Control for Deterministic Confounding
by: Xu, Liyuan, et al.
Published: (2023)
by: Xu, Liyuan, et al.
Published: (2023)
Perturbative methods for non-parametric instrumental variable
by: Bu, Wei, et al.
Published: (2026)
by: Bu, Wei, et al.
Published: (2026)
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
by: Zenati, Houssam, et al.
Published: (2026)
by: Zenati, Houssam, et al.
Published: (2026)
Spectral Representation for Causal Estimation with Hidden Confounders
by: Sun, Haotian, et al.
Published: (2024)
by: Sun, Haotian, et al.
Published: (2024)
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
by: Xu, Liyuan, et al.
Published: (2021)
by: Xu, Liyuan, et al.
Published: (2021)
Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
by: Zenati, Houssam, et al.
Published: (2025)
by: Zenati, Houssam, et al.
Published: (2025)
Spectral Souping: A Unified Framework for Online Preference Alignment
by: Chow, Yinlam, et al.
Published: (2026)
by: Chow, Yinlam, et al.
Published: (2026)
Interventional Processes for Causal Uncertainty Quantification
by: Dance, Hugh, et al.
Published: (2024)
by: Dance, Hugh, et al.
Published: (2024)
Kernel Treatment Effects with Adaptively Collected Data
by: Zenati, Houssam, et al.
Published: (2025)
by: Zenati, Houssam, et al.
Published: (2025)
Foundations of Multivariate Distributional Reinforcement Learning
by: Wiltzer, Harley, et al.
Published: (2024)
by: Wiltzer, Harley, et al.
Published: (2024)
Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
by: Singh, Rahul, et al.
Published: (2021)
by: Singh, Rahul, et al.
Published: (2021)
On the Optimality of Misspecified Spectral Algorithms
by: Zhang, Haobo, et al.
Published: (2023)
by: Zhang, Haobo, et al.
Published: (2023)
Fast and Scalable Score-Based Kernel Calibration Tests
by: Glaser, Pierre, et al.
Published: (2025)
by: Glaser, Pierre, et al.
Published: (2025)
Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
by: Chen, Zonghao, et al.
Published: (2025)
by: Chen, Zonghao, et al.
Published: (2025)
Deep MMD Gradient Flow without adversarial training
by: Galashov, Alexandre, et al.
Published: (2024)
by: Galashov, Alexandre, et al.
Published: (2024)
Learning Continually by Spectral Regularization
by: Lewandowski, Alex, et al.
Published: (2024)
by: Lewandowski, Alex, et al.
Published: (2024)
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
by: Mollenhauer, Mattes, et al.
Published: (2022)
by: Mollenhauer, Mattes, et al.
Published: (2022)
Controlling Moments with Kernel Stein Discrepancies
by: Kanagawa, Heishiro, et al.
Published: (2022)
by: Kanagawa, Heishiro, et al.
Published: (2022)
Accelerated Diffusion Models via Speculative Sampling
by: De Bortoli, Valentin, et al.
Published: (2025)
by: De Bortoli, Valentin, et al.
Published: (2025)
A Unified Data Representation Learning for Non-parametric Two-sample Testing
by: Tian, Xunye, et al.
Published: (2024)
by: Tian, Xunye, et al.
Published: (2024)
Learn to Guide Your Diffusion Model
by: Galashov, Alexandre, et al.
Published: (2025)
by: Galashov, Alexandre, et al.
Published: (2025)
Optimal Rates in Continual Linear Regression via Increasing Regularization
by: Levinstein, Ran, et al.
Published: (2025)
by: Levinstein, Ran, et al.
Published: (2025)
Jacobian Regularization Stabilizes Long-Term Integration of Neural Differential Equations
by: Janvier, Maya, et al.
Published: (2026)
by: Janvier, Maya, et al.
Published: (2026)
Similar Items
-
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
by: Li, Zhu, et al.
Published: (2023) -
Regularized least squares learning with heavy-tailed noise is minimax optimal
by: Mollenhauer, Mattes, et al.
Published: (2025) -
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
by: Wornbard, Jakub, et al.
Published: (2026) -
Nonlinear Meta-Learning Can Guarantee Faster Rates
by: Meunier, Dimitri, et al.
Published: (2023) -
Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
by: Meunier, Dimitri, et al.
Published: (2024)