Cover Image

Instance-optimal stochastic convex optimization: Can we improve upon sample-average and robust stochastic approximation?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Liwei, Pananjady, Ashwin
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multiscale replay: A robust algorithm for stochastic variational inequalities with a Markovian buffer
by: Nakul, Milind, et al.
Published: (2026)

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
by: Mou, Wenlong, et al.
Published: (2021)

Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
by: Le, Tam
Published: (2025)

Learning based convex approximation for constrained parametric optimization
by: Liu, Kang, et al.
Published: (2025)

First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
by: Lu, Zhaosong, et al.
Published: (2025)

A stochastic gradient method for trilevel optimization
by: Giovannelli, Tommaso, et al.
Published: (2025)

Multi-cut stochastic approximation methods for solving stochastic convex composite optimization
by: Liang, Jiaming, et al.
Published: (2025)

Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
by: Godichon-Baggioni, Antoine, et al.
Published: (2024)

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
by: Jentzen, Arnulf, et al.
Published: (2025)

Dealing with unbounded gradients in stochastic saddle-point optimization
by: Neu, Gergely, et al.
Published: (2024)

Primal-dual algorithm for contextual stochastic combinatorial optimization
by: Bouvier, Louis, et al.
Published: (2025)

A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
by: Liu, Wei, et al.
Published: (2025)

qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
by: Renganathan, Ashwin, et al.
Published: (2023)

A theoretical and empirical study of new adaptive algorithms with additional momentum steps and shifted updates for stochastic non-convex optimization
by: Alecsa, Cristian Daniel
Published: (2021)

A constrained optimization approach to improve robustness of neural networks
by: Zhao, Shudian, et al.
Published: (2024)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
by: Dereich, Steffen, et al.
Published: (2025)

Empirical and computer-aided robustness analysis of long-step and accelerated methods in smooth convex optimization
by: Vernimmen, Pierre, et al.
Published: (2025)

Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing
by: Lou, Mengqi, et al.
Published: (2024)

An SDE perspective on stochastic convex optimization
by: Maulen-Soto, Rodrigo, et al.
Published: (2022)

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
by: Dagréou, Mathieu, et al.
Published: (2022)

Distributionally robust stochastic optimal control
by: Shapiro, Alexander, et al.
Published: (2024)

State evolution beyond first-order methods I: Rigorous predictions and finite-sample guarantees
by: Celentano, Michael, et al.
Published: (2025)

Solving a class of stochastic optimal control problems by physics-informed neural networks
by: Jiao, Zhe, et al.
Published: (2024)

A learning-based approach to stochastic optimal control under reach-avoid constraint
by: Ni, Tingting, et al.
Published: (2024)

Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
by: Chandrasekher, Kabir Aladin, et al.
Published: (2022)

A simple uniformly optimal method without line search for convex optimization
by: Li, Tianjiao, et al.
Published: (2023)

Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes
by: Lan, Guanghui, et al.
Published: (2024)

Non-geodesically-convex optimization in the Wasserstein space
by: Luu, Hoang Phuc Hau, et al.
Published: (2024)

An abstract effective convergence theorem for stochastic processes, with applications to stochastic approximation
by: Neri, Morenikeji, et al.
Published: (2025)

Accelerated stochastic approximation with state-dependent noise
by: Ilandarideva, Sasila, et al.
Published: (2023)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
by: Islam, H M Mohaimanul, et al.
Published: (2024)

Dynamic programming and dimensionality in convex stochastic optimization and control
by: Pennanen, Teemu, et al.
Published: (2025)

Adam with model exponential moving average is effective for nonconvex optimization
by: Ahn, Kwangjun, et al.
Published: (2024)

Stochastic set-valued optimization and its application to robust learning
by: Giovannelli, Tommaso, et al.
Published: (2026)

Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)

Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks
by: Jentzen, Arnulf, et al.
Published: (2024)

A minimax optimal control approach for robust neural ODEs
by: Cipriani, Cristina, et al.
Published: (2023)

Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024)

PACSBO: Probably approximately correct safe Bayesian optimization
by: Tokmak, Abdullah, et al.
Published: (2024)

ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
by: Liu, Yu, et al.
Published: (2025)