Copertina

From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Qiang, Zhou, Ruida, Shen, Yang, Liu, Tie
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
di: Liu, Renpu, et al.
Pubblicazione: (2024)

Refined PAC-Bayes Bounds for Offline Bandits
di: Gouverneur, Amaury, et al.
Pubblicazione: (2025)

Correlational Lagrangian Schrödinger Bridge: Learning Dynamics with Population-Level Regularization
di: You, Yuning, et al.
Pubblicazione: (2024)

SPIRE: Conditional Personalization for Federated Diffusion Generative Models
di: Ozkara, Kaan, et al.
Pubblicazione: (2025)

On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
di: Shen, Wei, et al.
Pubblicazione: (2024)

Corruption-Robust Offline Reinforcement Learning with General Function Approximation
di: Ye, Chenlu, et al.
Pubblicazione: (2023)

On the Learnability of Offline Model-Based Optimization: A Ranking Perspective
di: Lyu, Shen-Huan, et al.
Pubblicazione: (2026)

Generalized Preference Optimization: A Unified Approach to Offline Alignment
di: Tang, Yunhao, et al.
Pubblicazione: (2024)

ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
di: Ozkara, Kaan, et al.
Pubblicazione: (2024)

An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains
di: Zhou, Ruida, et al.
Pubblicazione: (2024)

The Offline-Frontier Shift: Diagnosing Distributional Limits in Generative Multi-Objective Optimization
di: Holly, Stephanie, et al.
Pubblicazione: (2026)

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
di: Viallard, Paul, et al.
Pubblicazione: (2024)

Harnessing the Power of Federated Learning in Federated Contextual Bandits
di: Shi, Chengshuai, et al.
Pubblicazione: (2023)

Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
di: Dai, Yang, et al.
Pubblicazione: (2024)

A Distributional-Lifting Theorem for PAC Learning
di: Blanc, Guy, et al.
Pubblicazione: (2025)

Offline Stochastic Optimization of Black-Box Objective Functions
di: Dong, Juncheng, et al.
Pubblicazione: (2024)

Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
di: Levy, Orin, et al.
Pubblicazione: (2026)

Offline Trajectory Optimization for Offline Reinforcement Learning
di: Zhao, Ziqi, et al.
Pubblicazione: (2024)

Offline Model-Based Optimization: Comprehensive Review
di: Kim, Minsu, et al.
Pubblicazione: (2025)

Distributionally Robust PAC-Bayesian Control
di: Herceg, Domagoj, et al.
Pubblicazione: (2026)

Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
di: Aouali, Imad, et al.
Pubblicazione: (2024)

Diffusion Models as Optimizers for Efficient Planning in Offline RL
di: Huang, Renming, et al.
Pubblicazione: (2024)

Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
di: Li, Shangzhe, et al.
Pubblicazione: (2026)

Cost-Aware Optimal Pairwise Pure Exploration
di: Wu, Di, et al.
Pubblicazione: (2025)

Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond
di: Zhang, Lijun, et al.
Pubblicazione: (2023)

Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization
di: Yao, Michael S., et al.
Pubblicazione: (2025)

Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach
di: Boroujeni, Mahrokh Ghoddousi, et al.
Pubblicazione: (2024)

Towards Efficient Contrastive PAC Learning
di: Shen, Jie
Pubblicazione: (2025)

Adversarially Robust PAC Learnability of Real-Valued Functions
di: Attias, Idan, et al.
Pubblicazione: (2022)

Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise
di: Zeng, Shiwei, et al.
Pubblicazione: (2023)

Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
di: Kuba, Jakub Grudzien, et al.
Pubblicazione: (2024)

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
di: Cen, Shicong, et al.
Pubblicazione: (2024)

Dual Alignment Maximin Optimization for Offline Model-based RL
di: Zhou, Chi, et al.
Pubblicazione: (2025)

Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective
di: Yang, Yu-Wei, et al.
Pubblicazione: (2025)

BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
di: Zhao, Yilong, et al.
Pubblicazione: (2024)

ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge
di: Dao, Manh Cuong, et al.
Pubblicazione: (2025)

PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders
di: Guo, Ruixin, et al.
Pubblicazione: (2025)

Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
di: Wu, Yi-Shan, et al.
Pubblicazione: (2024)

Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
di: Yun, Taeyoung, et al.
Pubblicazione: (2024)

Improving Generalization of Complex Models under Unbounded Loss Using PAC-Bayes Bounds
di: Zhang, Xitong, et al.
Pubblicazione: (2023)