Cover Image

Near-Optimality of Contrastive Divergence Algorithms

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Glaser, Pierre, Huang, Kevin Han, Gretton, Arthur
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fast and Scalable Score-Based Kernel Calibration Tests
by: Glaser, Pierre, et al.
Published: (2025)

Regularized $f$-Divergence Kernel Tests
by: Ribero, Mónica, et al.
Published: (2026)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
by: Li, Zhu, et al.
Published: (2023)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
by: Meunier, Dimitri, et al.
Published: (2024)

(De)-regularized Maximum Mean Discrepancy Gradient Flow
by: Chen, Zonghao, et al.
Published: (2024)

Kernel Single Proxy Control for Deterministic Confounding
by: Xu, Liyuan, et al.
Published: (2023)

Perturbative methods for non-parametric instrumental variable
by: Bu, Wei, et al.
Published: (2026)

Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
by: Zenati, Houssam, et al.
Published: (2026)

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
by: Meunier, Dimitri, et al.
Published: (2024)

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
by: Kim, Juno, et al.
Published: (2025)

Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
by: Zenati, Houssam, et al.
Published: (2025)

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
by: Xu, Liyuan, et al.
Published: (2021)

Kernel Treatment Effects with Adaptively Collected Data
by: Zenati, Houssam, et al.
Published: (2025)

Interventional Processes for Causal Uncertainty Quantification
by: Dance, Hugh, et al.
Published: (2024)

Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
by: Singh, Rahul, et al.
Published: (2021)

Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
by: Chen, Zonghao, et al.
Published: (2025)

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
by: Wornbard, Jakub, et al.
Published: (2026)

Deep MMD Gradient Flow without adversarial training
by: Galashov, Alexandre, et al.
Published: (2024)

Controlling Moments with Kernel Stein Discrepancies
by: Kanagawa, Heishiro, et al.
Published: (2022)

Near Optimal Inference for the Best-Performing Algorithm
by: Painsky, Amichai
Published: (2025)

Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
by: Iwazaki, Shogo
Published: (2026)

On Uncertainty Quantification for Near-Bayes Optimal Algorithms
by: Wang, Ziyu, et al.
Published: (2024)

Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning
by: Li, Zhenghao, et al.
Published: (2025)

Accelerated Diffusion Models via Speculative Sampling
by: De Bortoli, Valentin, et al.
Published: (2025)

Sobolev Regularized MMD Gradient Flow
by: Tian, Chenyang, et al.
Published: (2026)

Near-Optimal Algorithms for Omniprediction
by: Okoroafor, Princewill, et al.
Published: (2025)

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
by: Dagréou, Mathieu, et al.
Published: (2023)

On the connection between Noise-Contrastive Estimation and Contrastive Divergence
by: Olmin, Amanda, et al.
Published: (2024)

Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment
by: Hu, Bingshan, et al.
Published: (2021)

Nonlinear Meta-Learning Can Guarantee Faster Rates
by: Meunier, Dimitri, et al.
Published: (2023)

Foundations of Multivariate Distributional Reinforcement Learning
by: Wiltzer, Harley, et al.
Published: (2024)

Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
by: Iwazaki, Shogo, et al.
Published: (2024)

Conditional Bayesian Quadrature
by: Chen, Zonghao, et al.
Published: (2024)

Composite Goodness-of-fit Tests with Kernels
by: Key, Oscar, et al.
Published: (2021)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
by: Lu, Miao, et al.
Published: (2024)

Fast Best-in-Class Regret for Contextual Bandits
by: Girard, Samuel, et al.
Published: (2025)

Density Ratio-Free Doubly Robust Proxy Causal Learning
by: Bozkurt, Bariscan, et al.
Published: (2025)

Density Ratio-based Proxy Causal Learning Without Density Ratios
by: Bozkurt, Bariscan, et al.
Published: (2025)

Spectral Representation for Causal Estimation with Hidden Confounders
by: Sun, Haotian, et al.
Published: (2024)

Sample-Near-Optimal Agnostic Boosting with Improved Running Time
by: da Cunha, Arthur, et al.
Published: (2026)