Imagen de Portada

Adaptive Learning-based Surrogate Method for Stochastic Programs with Implicitly Decision-dependent Uncertainty

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shen, Boyang, Liu, Junyi
Formato:	Preprint
Publicado:	2025
Materias:	Optimization and Control Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information
por: Zhang, Yiyang, et al.
Publicado: (2023)

A Reliability Theory of Compromise Decisions for Large-Scale Stochastic Programs
por: Diao, Shuotao, et al.
Publicado: (2024)

A Gradient Guided Diffusion Framework for Chance Constrained Programming
por: Zhang, Boyang, et al.
Publicado: (2025)

A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
por: Sadana, Utsav, et al.
Publicado: (2023)

TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction
por: Fang, Yuchen, et al.
Publicado: (2026)

Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
por: Im, Hyungki, et al.
Publicado: (2025)

Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
por: Akhtiamov, Danil, et al.
Publicado: (2026)

Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
por: Grazzi, Riccardo, et al.
Publicado: (2024)

Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
por: Zhang, Siyuan, et al.
Publicado: (2024)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
por: Orvieto, Antonio, et al.
Publicado: (2024)

Breaking the Stochasticity Barrier: An Adaptive Variance-Reduced Method for Variational Inequalities
por: Jeong, Yungi, et al.
Publicado: (2026)

Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming
por: Lu, Haihao, et al.
Publicado: (2021)

Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
por: Shen, Han, et al.
Publicado: (2024)

Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
por: Liu, Wei, et al.
Publicado: (2025)

Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
por: Mazumdar, Abhijit, et al.
Publicado: (2024)

A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
por: Claeys, Louis, et al.
Publicado: (2026)

Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
por: Hu, Yifan, et al.
Publicado: (2020)

Adaptive Federated Minimax Optimization with Lower Complexities
por: Huang, Feihu, et al.
Publicado: (2022)

Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
por: Liu, Zijian, et al.
Publicado: (2023)

Online Reinforcement Learning in Markov Decision Process Using Linear Programming
por: Leon, Vincent, et al.
Publicado: (2023)

Recent Developments in Machine Learning Methods for Stochastic Control and Games
por: Hu, Ruimeng, et al.
Publicado: (2023)

SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
por: Liu, Kang, et al.
Publicado: (2026)

Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization
por: Gong, Xiaochuan, et al.
Publicado: (2025)

The L-Shaped Method for Stochastic Programs with Decision-Dependent Uncertainty
por: Pantuso, Giovanni, et al.
Publicado: (2025)

Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization
por: Sato, Naoki, et al.
Publicado: (2023)

Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics
por: He, Zhiyu, et al.
Publicado: (2025)

In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
por: Liu, Zijian
Publicado: (2026)

The Price of Adaptivity in Stochastic Convex Optimization
por: Carmon, Yair, et al.
Publicado: (2024)

Stochastic Gradient Descent with Adaptive Data
por: Che, Ethan, et al.
Publicado: (2024)

A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization
por: Berahas, Albert S., et al.
Publicado: (2023)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
por: Ding, Kuangyu, et al.
Publicado: (2023)

Surrogate-based optimization of system architectures subject to hidden constraints
por: Bussemaker, Jasper, et al.
Publicado: (2025)

Flipping-based Policy for Chance-Constrained Markov Decision Processes
por: Shen, Xun, et al.
Publicado: (2024)

Surrogate-based Optimization via Clustering for Box-Constrained Problems
por: Ahmad, Maaz, et al.
Publicado: (2026)

Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contamination
por: You, Yulei, et al.
Publicado: (2026)

Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity
por: Umeda, Hikaru, et al.
Publicado: (2025)

Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
por: Mukherjee, Amartya, et al.
Publicado: (2025)

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach
por: Fernando, Heshan, et al.
Publicado: (2022)

Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning
por: Zeng, Sihan, et al.
Publicado: (2024)

Stochastic Adaptive Gradient Descent Without Descent
por: Aujol, Jean-François, et al.
Publicado: (2025)