Copertina

Stochastic Optimization with Optimal Importance Sampling

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Aolaritei, Liviu, Van Parys, Bart P. G., Lam, Henry, Jordan, Michael I.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Optimization and Control Machine Learning Statistics Theory
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Stopping Rules for Stochastic Gradient Descent via Anytime-Valid Confidence Sequences
di: Aolaritei, Liviu, et al.
Pubblicazione: (2025)

Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
di: Bennouna, Amine, et al.
Pubblicazione: (2021)

Efficient Data-Driven Optimization with Noisy Data
di: Van Parys, Bart P. G.
Pubblicazione: (2021)

Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
di: Aolaritei, Liviu, et al.
Pubblicazione: (2022)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
di: Gao, Yihang, et al.
Pubblicazione: (2025)

Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
di: Shafiee, Soroosh, et al.
Pubblicazione: (2023)

On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning
di: Ruan, Feng
Pubblicazione: (2024)

Gradient Equilibrium in Online Learning: Theory and Applications
di: Angelopoulos, Anastasios N., et al.
Pubblicazione: (2025)

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
di: Li, Gen, et al.
Pubblicazione: (2021)

The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
di: Huo, Dongyan, et al.
Pubblicazione: (2024)

From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective
di: Chan, Gabriel, et al.
Pubblicazione: (2024)

$K$-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control
di: Giegrich, Michael, et al.
Pubblicazione: (2023)

Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
di: Zhong, Yanjie, et al.
Pubblicazione: (2024)

Error Analysis of Triangular Optimal Transport Maps for Filtering
di: Al-Jarrah, Mohammad, et al.
Pubblicazione: (2025)

Learning an Optimal Assortment Policy under Observational Data
di: Han, Yuxuan, et al.
Pubblicazione: (2025)

A New Perspective On Denoising Based On Optimal Transport
di: Trillos, Nicolas Garcia, et al.
Pubblicazione: (2023)

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

An Elementary Proof of the Near Optimality of LogSumExp Smoothing
di: Samakhoana, Thabo, et al.
Pubblicazione: (2025)

High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
di: Armacki, Aleksandar, et al.
Pubblicazione: (2023)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
di: Collins-Woodfin, Elizabeth, et al.
Pubblicazione: (2024)

Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
di: Liang, Tengyuan, et al.
Pubblicazione: (2024)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
di: Roy, Somjit, et al.
Pubblicazione: (2026)

An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling
di: Huang, Xunpeng, et al.
Pubblicazione: (2024)

Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
di: Nazari, Parvin, et al.
Pubblicazione: (2025)

On the Sample Complexity of Set Membership Estimation for Linear Systems with Disturbances Bounded by Convex Sets
di: Xu, Haonan, et al.
Pubblicazione: (2024)

Federated Optimization of Smooth Loss Functions
di: Jadbabaie, Ali, et al.
Pubblicazione: (2022)

Certified Multi-Fidelity Zeroth-Order Optimization
di: de Montbrun, Étienne, et al.
Pubblicazione: (2023)

Robust Assortment Optimization from Observational Data
di: Lu, Miao, et al.
Pubblicazione: (2026)

Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
di: Saldi, Naci, et al.
Pubblicazione: (2025)

Trajectory-Restricted Optimization Conditions and Geometry-Aware Linear Convergence
di: Chaudhry, Faris, et al.
Pubblicazione: (2026)

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
di: Yang, Zhuoran, et al.
Pubblicazione: (2020)

Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
di: Milz, Johannes, et al.
Pubblicazione: (2025)

Robust Mean Estimation for Optimization: The Impact of Heavy Tails
di: van Parys, Bart P. G., et al.
Pubblicazione: (2025)

Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
di: Puchkin, Nikita, et al.
Pubblicazione: (2023)

Optimal transport natural gradient for statistical manifolds with continuous sample space
di: Chen, Yifan, et al.
Pubblicazione: (2018)

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

4+3 Phases of Compute-Optimal Neural Scaling Laws
di: Paquette, Elliot, et al.
Pubblicazione: (2024)

Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
di: Du, Xinchen, et al.
Pubblicazione: (2025)

Derivative-Free Sequential Quadratic Programming for Equality-Constrained Stochastic Optimization
di: Na, Sen
Pubblicazione: (2025)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
di: Li, Gen, et al.
Pubblicazione: (2020)