Copertina

Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Cutler, Joshua, Díaz, Mateo, Drusvyatskiy, Dmitriy
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Optimization and Control Machine Learning 90C15, 90C25
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

The radius of statistical efficiency
di: Cutler, Joshua, et al.
Pubblicazione: (2024)

An Inexact Halpern Iteration with Application to Distributionally Robust Optimization
di: Liang, Ling, et al.
Pubblicazione: (2024)

Achieving $\tilde{\mathcal{O}}(1/N)$ Optimality Gap in Restless Bandits through Gaussian Approximation
di: Yan, Chen, et al.
Pubblicazione: (2024)

Boosted Stochastic Frank-Wolfe for Constrained Nonconvex Optimization
di: Nandhan, Navil, et al.
Pubblicazione: (2026)

Distributionally Robust Optimization over Wasserstein Balls with i.i.d. Structure
di: Kharitenko, Andrey, et al.
Pubblicazione: (2025)

A Normal Map-Based Proximal Stochastic Gradient Method: Convergence and Identification Properties
di: Qiu, Junwen, et al.
Pubblicazione: (2023)

Stochastic optimization over proximally smooth sets
di: Davis, Damek, et al.
Pubblicazione: (2020)

Metric Entropy-Free Sample Complexity Bounds for Sample Average Approximation in Convex Stochastic Programming
di: Liu, Hongcheng, et al.
Pubblicazione: (2024)

Bias-Variance Trade-off for Clipped Stochastic First-Order Methods: From Bounded Variance to Infinite Mean
di: He, Chuan
Pubblicazione: (2025)

Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions
di: Kurtz, Jannis, et al.
Pubblicazione: (2026)

DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime
di: Park, Jongho, et al.
Pubblicazione: (2023)

Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization
di: Smith, Noel, et al.
Pubblicazione: (2026)

Shuffling the Stochastic Mirror Descent via Dual Lipschitz Continuity and Kernel Conditioning
di: Qiu, Junwen, et al.
Pubblicazione: (2026)

Stochastic First-Order Methods with Non-smooth and Non-Euclidean Proximal Terms for Nonconvex High-Dimensional Stochastic Optimization
di: Xie, Yue, et al.
Pubblicazione: (2024)

Bregman Stochastic Proximal Point Algorithm with Variance Reduction
di: Traoré, Cheik, et al.
Pubblicazione: (2025)

Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions
di: Tran-Dinh, Quoc, et al.
Pubblicazione: (2026)

Stochastic Gradient Descent Revisited
di: Louzi, Azar
Pubblicazione: (2024)

Chance constrained directional models in stochastic data envelopment analysis
di: Bolos, Vicente J., et al.
Pubblicazione: (2024)

Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions
di: Hikima, Yuya, et al.
Pubblicazione: (2024)

Forecasting Outside the Box: Application-Driven Optimal Pointwise Forecasts for Stochastic Optimization
di: Homem-de-Mello, Tito, et al.
Pubblicazione: (2024)

Concave Certificates: Geometric Framework for Distributionally Robust Risk and Complexity Analysis
di: Chu, Hong T. M.
Pubblicazione: (2026)

Multivariate approximation by polynomial and generalised rational functions
di: Millán, R. Díaz, et al.
Pubblicazione: (2021)

A Regression-Based Prediction-Correction Method for Stochastic Time-Varying Optimization Problems
di: Kamijima, Tomoya, et al.
Pubblicazione: (2025)

Stochastic Block Bregman Projection with Polyak-like Stepsize for Possibly Inconsistent Convex Feasibility Problems
di: Zhang, Lu, et al.
Pubblicazione: (2026)

Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
di: Fatkhullin, Ilyas, et al.
Pubblicazione: (2024)

A Single-Loop Stochastic Proximal Quasi-Newton Method for Large-Scale Nonsmooth Convex Optimization
di: Song, Yongcun, et al.
Pubblicazione: (2024)

A conditional gradient homotopy method with applications to Semidefinite Programming
di: Dvurechensky, Pavel, et al.
Pubblicazione: (2022)

Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints
di: Lin, Zhenwei, et al.
Pubblicazione: (2022)

Generalized Optimistic Methods for Convex-Concave Saddle Point Problems
di: Jiang, Ruichen, et al.
Pubblicazione: (2022)

Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets
di: Liu, Ning, et al.
Pubblicazione: (2023)

NewVEM: A Newton Vertex Exchange Method for a Class of Constrained Self-Concordant Minimization Problems
di: Liang, Ling, et al.
Pubblicazione: (2024)

Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting
di: Prakhya, Karthik, et al.
Pubblicazione: (2026)

Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approach
di: Kassing, Sebastian, et al.
Pubblicazione: (2026)

Stochastic interior-point methods for smooth conic optimization with applications
di: He, Chuan, et al.
Pubblicazione: (2024)

Variance-Reduced Fast Operator Splitting Methods for Generalized Equations
di: Tran-Dinh, Quoc
Pubblicazione: (2025)

Variance-Reduced Fast Krasnoselkii-Mann Methods for Finite-Sum Root-Finding Problems
di: Tran-Dinh, Quoc
Pubblicazione: (2024)

Accelerated Extragradient-Type Methods -- Part 2: Generalization and Sublinear Convergence Rates under Co-Hypomonotonicity
di: Tran-Dinh, Quoc, et al.
Pubblicazione: (2025)

Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates
di: Tran-Dinh, Quoc, et al.
Pubblicazione: (2024)

Policy Optimization over General State and Action Spaces
di: Ju, Caleb, et al.
Pubblicazione: (2022)

Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth
di: Davis, Damek, et al.
Pubblicazione: (2024)