Cover Image

Tame Riemannian Stochastic Approximation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Aspman, Johannes, Kungurtsev, Vyacheslav, Seraji, Reza Roohi
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Taming Binarized Neural Networks and Mixed-Integer Programs
by: Aspman, Johannes, et al.
Published: (2023)

Deep Learning as the Disciplined Construction of Tame Objects
by: Bareilles, Gilles, et al.
Published: (2025)

Stochastic Langevin Differential Inclusions with Applications to Machine Learning
by: Difonzo, Fabio V., et al.
Published: (2022)

Piecewise Polynomial Regression of Tame Functions via Integer Programming
by: Bareilles, Gilles, et al.
Published: (2023)

Stochastic Approximation for Expectation Objective and Expectation Inequality-Constrained Nonconvex Optimization
by: Facchinei, Francisco, et al.
Published: (2023)

A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
by: Curtis, Frank E., et al.
Published: (2023)

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
by: Kimiaei, Morteza, et al.
Published: (2025)

Probabilistic Iterative Hard Thresholding for Sparse Learning
by: Bergamaschi, Matteo, et al.
Published: (2024)

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
by: Mangold, Paul, et al.
Published: (2024)

Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management
by: Mastropietro, Daniel, et al.
Published: (2026)

Quasi-Ergodic Control of Multi-Periodic Autoregressive Processes: Formulation and Examples
by: Kungurtsev, Vyacheslav
Published: (2025)

A Three-Tier Time-Scale Architecture for Controlling Complex Nonlinear Systems
by: Kungurtsev, Vyacheslav
Published: (2026)

Riemannian Stochastic Gradient Method for Nested Composition Optimization
by: Zhang, Dewei, et al.
Published: (2022)

Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
by: Dodd, Daniel, et al.
Published: (2024)

Exponential Concentration in Stochastic Approximation
by: Law, Kody, et al.
Published: (2022)

Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
by: Oowada, Kanata, et al.
Published: (2025)

Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds
by: Sahinoglu, Emre, et al.
Published: (2025)

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
by: Levin, Ilya, et al.
Published: (2026)

Stochastic Approximation with Block Coordinate Optimal Stepsizes
by: Jiang, Tao, et al.
Published: (2025)

Online Covariance Estimation in Nonsmooth Stochastic Approximation
by: Jiang, Liwei, et al.
Published: (2025)

Efficient Stochastic Approximation of Minimax Excess Risk Optimization
by: Zhang, Lijun, et al.
Published: (2023)

Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
by: Chen, Zaiwei, et al.
Published: (2023)

Stochastic Approximation Methods for Distortion Risk Measure Optimization
by: Jiang, Jinyang, et al.
Published: (2025)

A Retrospective Approximation Approach for Smooth Stochastic Optimization
by: Newton, David, et al.
Published: (2021)

Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
by: Vaswani, Sharan, et al.
Published: (2025)

(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum
by: Dang, Anh, et al.
Published: (2024)

Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise
by: Allmeier, Sebastian, et al.
Published: (2024)

Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
by: Huo, Dongyan, et al.
Published: (2022)

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

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
by: Kwon, Jeongyeol, et al.
Published: (2023)

$ψ$DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning
by: Ziu, Klea, et al.
Published: (2024)

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
by: Vaswani, Sharan, et al.
Published: (2021)

Stochastic Approximation with Biased MCMC for Expectation Maximization
by: Gruffaz, Samuel, et al.
Published: (2024)

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
by: Haque, Shaan Ul, et al.
Published: (2024)

Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

First and Second Order Approximations to Stochastic Gradient Descent Methods with Momentum Terms
by: Lu, Eric
Published: (2025)

Riemannian Dueling Optimization
by: Ren, Yuxuan, et al.
Published: (2026)

Convergence Rate in Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence
by: Chen, Zixi, et al.
Published: (2025)

Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)

Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
by: Hu, Jie, et al.
Published: (2024)