Imagen de Portada

Distributional Bellman Operators over Mean Embeddings

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wenliang, Li Kevin, Delétang, Grégoire, Aitchison, Matthew, Hutter, Marcus, Ruoss, Anian, Gretton, Arthur, Rowland, Mark
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Learning Universal Predictors
por: Grau-Moya, Jordi, et al.
Publicado: (2024)

Why is prompting hard? Understanding prompts on binary sequence predictors
por: Wenliang, Li Kevin, et al.
Publicado: (2025)

Language Modeling Is Compression
por: Delétang, Grégoire, et al.
Publicado: (2023)

Understanding Prompt Tuning and In-Context Learning via Meta-Learning
por: Genewein, Tim, et al.
Publicado: (2025)

Amortized Planning with Large-Scale Transformers: A Case Study on Chess
por: Ruoss, Anian, et al.
Publicado: (2024)

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

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

Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data
por: Heurtel-Depeiges, David, et al.
Publicado: (2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
por: Rowland, Mark, et al.
Publicado: (2024)

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

Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
por: Bozkurt, Bariscan, et al.
Publicado: (2026)

LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations
por: Ruoss, Anian, et al.
Publicado: (2024)

A Distributional Analogue to the Successor Representation
por: Wiltzer, Harley, et al.
Publicado: (2024)

Evaluating Frontier Models for Dangerous Capabilities
por: Phuong, Mary, et al.
Publicado: (2024)

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

On the Wasserstein Gradient Flow Interpretation of Drifting Models
por: Gretton, Arthur, et al.
Publicado: (2026)

Near-Optimality of Contrastive Divergence Algorithms
por: Glaser, Pierre, et al.
Publicado: (2025)

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

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

A Spectral Revisit of the Distributional Bellman Operator under the Cramér Metric
por: Wang, Keru, et al.
Publicado: (2026)

Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
por: Li, Yangming, et al.
Publicado: (2024)

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

Parameterized Projected Bellman Operator
por: Vincent, Théo, et al.
Publicado: (2023)

Distributional Diffusion Models with Scoring Rules
por: De Bortoli, Valentin, et al.
Publicado: (2025)

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

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

Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning
por: Omura, Motoki, et al.
Publicado: (2025)

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

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

Your Policy Regularizer is Secretly an Adversary
por: Brekelmans, Rob, et al.
Publicado: (2022)

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

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

Why you don't overfit, and don't need Bayes if you only train for one epoch
por: Aitchison, Laurence
Publicado: (2024)

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

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

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

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

Distributional Off-policy Evaluation with Bellman Residual Minimization
por: Hong, Sungee, et al.
Publicado: (2024)

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

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