Imagen de Portada

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Dagréou, Mathieu, Ablin, Pierre, Vaiter, Samuel, Moreau, Thomas
Formato:	Preprint
Publicado:	2022
Materias:	Machine Learning Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
por: Dagréou, Mathieu, et al.
Publicado: (2023)

Proximal basin hopping: global optimization with guarantees
por: Lauga, Guillaume, et al.
Publicado: (2026)

Derivatives of Stochastic Gradient Descent in parametric optimization
por: Iutzeler, Franck, et al.
Publicado: (2024)

Fairness-informed Pareto Optimization : An Efficient Bilevel Framework
por: Tanji, Sofiane, et al.
Publicado: (2026)

Decision-focused predictions via pessimistic bilevel optimization: complexity and algorithms
por: Bucarey, Víctor, et al.
Publicado: (2023)

Variance reduction techniques for stochastic proximal point algorithms
por: Traoré, Cheik, et al.
Publicado: (2023)

Geometric and computational hardness of bilevel programming
por: Bolte, Jérôme, et al.
Publicado: (2024)

An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning
por: Salehi, Mohammad Sadegh, et al.
Publicado: (2023)

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

LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace
por: Yang, Yan, et al.
Publicado: (2024)

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

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
por: Ablin, Pierre, et al.
Publicado: (2023)

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

Faster Computation of Entropic Optimal Transport via Stable Low Frequency Modes
por: Chhaibi, Reda, et al.
Publicado: (2025)

Classification under strategic adversary manipulation using pessimistic bilevel optimisation
por: Benfield, David, et al.
Publicado: (2024)

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

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

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

Instance-optimal stochastic convex optimization: Can we improve upon sample-average and robust stochastic approximation?
por: Jiang, Liwei, et al.
Publicado: (2026)

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

Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables
por: Brevault, Loic, et al.
Publicado: (2023)

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

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

From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
por: Hurault, Samuel, et al.
Publicado: (2025)

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

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

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

Whiteness-based bilevel learning of regularization parameters in imaging
por: Santambrogio, Carlo, et al.
Publicado: (2024)

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

Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise
por: Dorn, Yuriy, et al.
Publicado: (2024)

Sinkhorn algorithms and linear programming solvers for optimal partial transport problems
por: Bai, Yikun
Publicado: (2024)

Derivative-free stochastic bilevel optimization for inverse problems
por: Staudigl, Mathias, et al.
Publicado: (2024)

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

Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
por: Martin, Andrea, et al.
Publicado: (2025)

An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems
por: Zhang, Huiling, et al.
Publicado: (2023)

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

Characterizations of inexact proximal operators
por: Lauga, Guillaume, et al.
Publicado: (2026)

Bayesian optimization as a flexible and efficient design framework for sustainable process systems
por: Paulson, Joel A., et al.
Publicado: (2024)

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

Local adapt-then-combine algorithms for distributed nonsmooth optimization: Achieving provable communication acceleration
por: Guo, Luyao, et al.
Publicado: (2026)