Copertina

Asymptotic regularity of a generalised stochastic Halpern scheme

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Pischke, Nicholas, Powell, Thomas
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Optimization and Control Machine Learning Probability
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Generalized fluctuation bounds for stochastic algorithms in the presence of compactness
di: Neri, Morenikeji, et al.
Pubblicazione: (2026)

Mean-square and linear convergence of a stochastic proximal point algorithm in metric spaces of nonpositive curvature
di: Pischke, Nicholas
Pubblicazione: (2025)

Convergence guarantees for stochastic algorithms solving non-unique problems in metric spaces
di: Pischke, Nicholas, et al.
Pubblicazione: (2026)

A quantitative Robbins-Siegmund theorem
di: Neri, Morenikeji, et al.
Pubblicazione: (2024)

Linear convergence of proximal descent schemes on the Wasserstein space
di: Lascu, Razvan-Andrei, et al.
Pubblicazione: (2024)

A stochastic gradient descent algorithm with random search directions
di: Gbaguidi, Eméric
Pubblicazione: (2025)

Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow
di: Li, Qin, et al.
Pubblicazione: (2024)

The generator gradient estimator is an adjoint state method for stochastic differential equations
di: Badolle, Quentin, et al.
Pubblicazione: (2024)

Function approximation by neural nets in the mean-field regime: Entropic regularization and controlled McKean-Vlasov dynamics
di: Tzen, Belinda, et al.
Pubblicazione: (2020)

On Busemann subgradient methods for stochastic minimization in Hadamard spaces
di: Pischke, Nicholas
Pubblicazione: (2026)

Weak convergence of the stochastic proximal point method in metric spaces
di: Pischke, Nicholas
Pubblicazione: (2026)

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

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
di: Liang, Luxu, et al.
Pubblicazione: (2024)

Convergence rate of Tsallis entropic regularized optimal transport
di: Suguro, Takeshi, et al.
Pubblicazione: (2023)

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

Stochastic Halpern iteration in normed spaces and applications to reinforcement learning
di: Bravo, Mario, et al.
Pubblicazione: (2024)

Fast Spawn\&Prune (FS\&P): Global convergence of stochastic conic particle gradient descent via birth/death process
di: De Castro, Yohann, et al.
Pubblicazione: (2026)

A Generalization Result for Convergence in Learning-to-Optimize
di: Sucker, Michael, et al.
Pubblicazione: (2024)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
di: Han, Yuze, et al.
Pubblicazione: (2024)

Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
di: Hu, Jie, et al.
Pubblicazione: (2024)

Weak Convergence Analysis of Online Neural Actor-Critic Algorithms
di: Lam, Samuel Chun-Hei, et al.
Pubblicazione: (2024)

Learning-Based Pricing and Matching for Two-Sided Queues
di: Yang, Zixian, et al.
Pubblicazione: (2024)

Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance
di: Gao, Rui, et al.
Pubblicazione: (2024)

Regret of exploratory policy improvement and $q$-learning
di: Tang, Wenpin, et al.
Pubblicazione: (2024)

On the SAGA algorithm with decreasing step
di: Fredes, Luis, et al.
Pubblicazione: (2024)

Convergence rates for the Adam optimizer
di: Dereich, Steffen, et al.
Pubblicazione: (2024)

Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry
di: Armacki, Aleksandar, et al.
Pubblicazione: (2024)

Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA
di: Zhang, Yixuan, et al.
Pubblicazione: (2024)

Model Predictive Control is Almost Optimal for Restless Bandit
di: Gast, Nicolas, et al.
Pubblicazione: (2024)

Mirror Descent-Ascent for mean-field min-max problems
di: Lascu, Razvan-Andrei, et al.
Pubblicazione: (2024)

A Fisher-Rao gradient flow for entropic mean-field min-max games
di: Lascu, Razvan-Andrei, et al.
Pubblicazione: (2024)

Structure Matters: Dynamic Policy Gradient
di: Klein, Sara, et al.
Pubblicazione: (2024)

Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits
di: Narasimha, Dheeraj, et al.
Pubblicazione: (2025)

Properties of Discrete Sliced Wasserstein Losses
di: Tanguy, Eloi, et al.
Pubblicazione: (2023)

Non-convex entropic mean-field optimization via Best Response flow
di: Lascu, Razvan-Andrei, et al.
Pubblicazione: (2025)

On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
di: Lazić, Petra, et al.
Pubblicazione: (2026)

Value Mirror Descent for Reinforcement Learning
di: Jia, Zhichao, et al.
Pubblicazione: (2026)

Improved sampling via learned diffusions
di: Richter, Lorenz, et al.
Pubblicazione: (2023)

ODE approximation for the Adam algorithm: General and overparametrized setting
di: Dereich, Steffen, et al.
Pubblicazione: (2025)