Optimization Insights into Deep Diagonal Linear Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Labarrière, Hippolyte, Molinari, Cesare, Rosasco, Lorenzo, Vega, Cristian, Villa, Silvia |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
by: Labarrière, Hippolyte, et al.
Published: (2026)
by: Labarrière, Hippolyte, et al.
Published: (2026)
Stochastic Zeroth order Descent with Structured Directions
by: Rando, Marco, et al.
Published: (2022)
by: Rando, Marco, et al.
Published: (2022)
Convergence of zeroth-order proximal point algorithms in the high-temperature regime
by: Naldi, Emanuele, et al.
Published: (2026)
by: Naldi, Emanuele, et al.
Published: (2026)
A Structured Tour of Optimization with Finite Differences
by: Rando, Marco, et al.
Published: (2025)
by: Rando, Marco, et al.
Published: (2025)
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
by: Caldarelli, Edoardo, et al.
Published: (2024)
by: Caldarelli, Edoardo, et al.
Published: (2024)
Iterative regularization in classification via hinge loss diagonal descent
by: Apidopoulos, Vassilis, et al.
Published: (2022)
by: Apidopoulos, Vassilis, et al.
Published: (2022)
Proximal basin hopping: global optimization with guarantees
by: Lauga, Guillaume, et al.
Published: (2026)
by: Lauga, Guillaume, et al.
Published: (2026)
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks
by: Papazov, Hristo, et al.
Published: (2024)
by: Papazov, Hristo, et al.
Published: (2024)
High-dimensional Limit of SGD for Diagonal Linear Networks
by: Malaxechebarría, Begoña García, et al.
Published: (2026)
by: Malaxechebarría, Begoña García, et al.
Published: (2026)
Strong Convergence of FISTA Iterates under H{ö}lderian and Quadratic Growth Conditions
by: Aujol, Jean-François, et al.
Published: (2024)
by: Aujol, Jean-François, et al.
Published: (2024)
A Structured Proximal Stochastic Variance Reduced Zeroth-order Algorithm
by: Rando, Marco, et al.
Published: (2025)
by: Rando, Marco, et al.
Published: (2025)
Model Consistency of the Iterative Regularization of Dual Ascent for Low-Complexity Regularization
by: Gao, Jie, et al.
Published: (2025)
by: Gao, Jie, et al.
Published: (2025)
Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
by: Qin, Zhen, et al.
Published: (2023)
by: Qin, Zhen, et al.
Published: (2023)
Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control
by: Caldarelli, Edoardo, et al.
Published: (2026)
by: Caldarelli, Edoardo, et al.
Published: (2026)
Learning Multi-Index Models with Hyper-Kernel Ridge Regression
by: Huang, Shuo, et al.
Published: (2025)
by: Huang, Shuo, et al.
Published: (2025)
Efficient Sparse PCA via Block-Diagonalization
by: Del Pia, Alberto, et al.
Published: (2024)
by: Del Pia, Alberto, et al.
Published: (2024)
SGD with Partial Hessian for Deep Neural Networks Optimization
by: Sun, Ying, et al.
Published: (2024)
by: Sun, Ying, et al.
Published: (2024)
On Learning the Optimal Regularization Parameter in Inverse Problems
by: Rodriguez, Jonathan Chirinos, et al.
Published: (2023)
by: Rodriguez, Jonathan Chirinos, et al.
Published: (2023)
Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information Analysis
by: Gomes, Damien Martins
Published: (2025)
by: Gomes, Damien Martins
Published: (2025)
Counterfactual Explanations for Linear Optimization
by: Kurtz, Jannis, et al.
Published: (2024)
by: Kurtz, Jannis, et al.
Published: (2024)
Diagonalizing the Softmax: Hadamard Initialization for Tractable Cross-Entropy Dynamics
by: Garrod, Connall, et al.
Published: (2025)
by: Garrod, Connall, et al.
Published: (2025)
Diagonal Linear Networks and the Lasso Regularization Path
by: Berthier, Raphaël
Published: (2025)
by: Berthier, Raphaël
Published: (2025)
Variance reduction techniques for stochastic proximal point algorithms
by: Traoré, Cheik, et al.
Published: (2023)
by: Traoré, Cheik, et al.
Published: (2023)
Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
by: Laus, Hannah, et al.
Published: (2025)
by: Laus, Hannah, et al.
Published: (2025)
Snacks: a fast large-scale kernel SVM solver
by: Tanji, Sofiane, et al.
Published: (2023)
by: Tanji, Sofiane, et al.
Published: (2023)
Accelerated Optimization Landscape of Linear-Quadratic Regulator
by: Feng, Lechen, et al.
Published: (2023)
by: Feng, Lechen, et al.
Published: (2023)
First-Order Methods for Linearly Constrained Bilevel Optimization
by: Kornowski, Guy, et al.
Published: (2024)
by: Kornowski, Guy, et al.
Published: (2024)
Local Linear Convergence of Infeasible Optimization with Orthogonal Constraints
by: Sun, Youbang, et al.
Published: (2024)
by: Sun, Youbang, et al.
Published: (2024)
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
by: Magnino, Lorenzo, et al.
Published: (2024)
by: Magnino, Lorenzo, et al.
Published: (2024)
Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks
by: Beneventano, Pierfrancesco, et al.
Published: (2025)
by: Beneventano, Pierfrancesco, et al.
Published: (2025)
Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints
by: Hutchinson, Spencer, et al.
Published: (2024)
by: Hutchinson, Spencer, et al.
Published: (2024)
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
by: Guo, Luyao, et al.
Published: (2023)
by: Guo, Luyao, et al.
Published: (2023)
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
by: Li, Bingheng, et al.
Published: (2024)
by: Li, Bingheng, et al.
Published: (2024)
Active Learning For Contextual Linear Optimization: A Margin-Based Approach
by: Liu, Mo, et al.
Published: (2023)
by: Liu, Mo, et al.
Published: (2023)
What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization
by: Bennouna, Omar, et al.
Published: (2025)
by: Bennouna, Omar, et al.
Published: (2025)
Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
by: Im, Hyungki, et al.
Published: (2025)
by: Im, Hyungki, et al.
Published: (2025)
Physics-Informed Neural Networks with Hard Linear Equality Constraints
by: Chen, Hao, et al.
Published: (2024)
by: Chen, Hao, et al.
Published: (2024)
Two-Timescale Optimization Framework for Sparse-Feedback Linear-Quadratic Optimal Control
by: Feng, Lechen, et al.
Published: (2024)
by: Feng, Lechen, et al.
Published: (2024)
Safe and Efficient Online Convex Optimization with Linear Budget Constraints and Partial Feedback
by: Liu, Shanqi, et al.
Published: (2024)
by: Liu, Shanqi, et al.
Published: (2024)
Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
by: Huang, Ruichuan, et al.
Published: (2025)
by: Huang, Ruichuan, et al.
Published: (2025)
Similar Items
-
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
by: Labarrière, Hippolyte, et al.
Published: (2026) -
Stochastic Zeroth order Descent with Structured Directions
by: Rando, Marco, et al.
Published: (2022) -
Convergence of zeroth-order proximal point algorithms in the high-temperature regime
by: Naldi, Emanuele, et al.
Published: (2026) -
A Structured Tour of Optimization with Finite Differences
by: Rando, Marco, et al.
Published: (2025) -
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
by: Caldarelli, Edoardo, et al.
Published: (2024)