Convergence Properties of Stochastic Hypergradients
Fuente:
arXiv
Saved in:
| Main Authors: | Grazzi, Riccardo, Pontil, Massimiliano, Salzo, Saverio |
|---|---|
| Format: | Preprint |
| Published: |
2020
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
by: Grazzi, Riccardo, et al.
Published: (2024)
by: Grazzi, Riccardo, et al.
Published: (2024)
AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm
by: Bojovic, Matia, et al.
Published: (2026)
by: Bojovic, Matia, et al.
Published: (2026)
Bilevel learning
by: Grazzi, Riccardo, et al.
Published: (2026)
by: Grazzi, Riccardo, et al.
Published: (2026)
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
by: Parletta, Daniela A., et al.
Published: (2022)
by: Parletta, Daniela A., et al.
Published: (2022)
Learning invariant representations of time-homogeneous stochastic dynamical systems
by: Kostic, Vladimir R., et al.
Published: (2023)
by: Kostic, Vladimir R., et al.
Published: (2023)
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
by: Siems, Julien, et al.
Published: (2025)
by: Siems, Julien, et al.
Published: (2025)
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
by: Grazzi, Riccardo, et al.
Published: (2024)
by: Grazzi, Riccardo, et al.
Published: (2024)
Variance reduction techniques for stochastic proximal point algorithms
by: Traoré, Cheik, et al.
Published: (2023)
by: Traoré, Cheik, et al.
Published: (2023)
Natural Hypergradient Descent: Algorithm Design, Convergence Analysis, and Parallel Implementation
by: Kong, Deyi, et al.
Published: (2026)
by: Kong, Deyi, et al.
Published: (2026)
Learning the Infinitesimal Generator of Stochastic Diffusion Processes
by: Kostic, Vladimir R., et al.
Published: (2024)
by: Kostic, Vladimir R., et al.
Published: (2024)
The iterates of FISTA convergence even under inexact computations and stochastic gradients
by: Salzo, Saverio
Published: (2025)
by: Salzo, Saverio
Published: (2025)
Glocal Hypergradient Estimation with Koopman Operator
by: Hataya, Ryuichiro, et al.
Published: (2024)
by: Hataya, Ryuichiro, et al.
Published: (2024)
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization
by: Venturini, Sara, et al.
Published: (2023)
by: Venturini, Sara, et al.
Published: (2023)
Efficient Bilevel Optimization with KFAC-Based Hypergradients
by: Liao, Disen, et al.
Published: (2026)
by: Liao, Disen, et al.
Published: (2026)
An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications
by: Mirzaei, Erfan, et al.
Published: (2025)
by: Mirzaei, Erfan, et al.
Published: (2025)
OptRot: Mitigating Weight Outliers via Data-Free Rotations for Post-Training Quantization
by: Gadhikar, Advait, et al.
Published: (2025)
by: Gadhikar, Advait, et al.
Published: (2025)
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
by: Devergne, Timothée, et al.
Published: (2024)
by: Devergne, Timothée, et al.
Published: (2024)
A randomized algorithm to solve reduced rank operator regression
by: Turri, Giacomo, et al.
Published: (2023)
by: Turri, Giacomo, et al.
Published: (2023)
Operator World Models for Reinforcement Learning
by: Novelli, Pietro, et al.
Published: (2024)
by: Novelli, Pietro, et al.
Published: (2024)
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
by: Ye, Zhenzhang, et al.
Published: (2024)
by: Ye, Zhenzhang, et al.
Published: (2024)
Toward Scalable and Valid Conditional Independence Testing with Spectral Representations
by: Frohlich, Alek, et al.
Published: (2025)
by: Frohlich, Alek, et al.
Published: (2025)
A conversion theorem and minimax optimality for continuum contextual bandits
by: Akhavan, Arya, et al.
Published: (2024)
by: Akhavan, Arya, et al.
Published: (2024)
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization
by: Dong, Youran, et al.
Published: (2025)
by: Dong, Youran, et al.
Published: (2025)
Is Mamba Capable of In-Context Learning?
by: Grazzi, Riccardo, et al.
Published: (2024)
by: Grazzi, Riccardo, et al.
Published: (2024)
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
by: Chen, Yuanyuan, et al.
Published: (2021)
by: Chen, Yuanyuan, et al.
Published: (2021)
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
by: Falk, John, et al.
Published: (2023)
by: Falk, John, et al.
Published: (2023)
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
by: Turri, Giacomo, et al.
Published: (2025)
by: Turri, Giacomo, et al.
Published: (2025)
Federated Learning with Hypergradient-based Online Update of Aggregation Weights
by: Nakai-Kasai, Ayano, et al.
Published: (2026)
by: Nakai-Kasai, Ayano, et al.
Published: (2026)
Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
by: Chu, Ya-Chi, et al.
Published: (2025)
by: Chu, Ya-Chi, et al.
Published: (2025)
Bi-Level Policy Optimization with Nyström Hypergradients
by: Prakash, Arjun, et al.
Published: (2025)
by: Prakash, Arjun, et al.
Published: (2025)
Learning State-Tracking from Code Using Linear RNNs
by: Siems, Julien, et al.
Published: (2026)
by: Siems, Julien, et al.
Published: (2026)
Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
by: Fearnhead, Paul, et al.
Published: (2024)
by: Fearnhead, Paul, et al.
Published: (2024)
Toeplitz Based Spectral Methods for Data-driven Dynamical Systems
by: Kostic, Vladimir R., et al.
Published: (2026)
by: Kostic, Vladimir R., et al.
Published: (2026)
Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence
by: Bonalli, Riccardo, et al.
Published: (2023)
by: Bonalli, Riccardo, et al.
Published: (2023)
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
by: Kostic, Vladimir R., et al.
Published: (2024)
by: Kostic, Vladimir R., et al.
Published: (2024)
An Improved Analysis of the Clipped Stochastic subGradient Method under Heavy-Tailed Noise
by: Parletta, Daniela Angela, et al.
Published: (2024)
by: Parletta, Daniela Angela, et al.
Published: (2024)
Hyperparameter Optimization in Machine Learning
by: Franceschi, Luca, et al.
Published: (2024)
by: Franceschi, Luca, et al.
Published: (2024)
Neural Conditional Probability for Uncertainty Quantification
by: Kostic, Vladimir R., et al.
Published: (2024)
by: Kostic, Vladimir R., et al.
Published: (2024)
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
by: Meunier, Dimitri, et al.
Published: (2025)
by: Meunier, Dimitri, et al.
Published: (2025)
From Offline to Online Memory-Free and Task-Free Continual Learning via Fine-Grained Hypergradients
by: Michel, Nicolas, et al.
Published: (2025)
by: Michel, Nicolas, et al.
Published: (2025)
Similar Items
-
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
by: Grazzi, Riccardo, et al.
Published: (2024) -
AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm
by: Bojovic, Matia, et al.
Published: (2026) -
Bilevel learning
by: Grazzi, Riccardo, et al.
Published: (2026) -
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
by: Parletta, Daniela A., et al.
Published: (2022) -
Learning invariant representations of time-homogeneous stochastic dynamical systems
by: Kostic, Vladimir R., et al.
Published: (2023)