Imagen de Portada

SpectraLDS: Provable Distillation for Linear Dynamical Systems

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shah, Devan, Fortgang, Shlomo, Druchyna, Sofiia, Hazan, Elad
Formato:	Preprint
Publicado:	2025
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 New Approach to Controlling Linear Dynamical Systems
por: Brahmbhatt, Anand, et al.
Publicado: (2025)

Efficient Spectral Control of Partially Observed Linear Dynamical Systems
por: Brahmbhatt, Anand, et al.
Publicado: (2025)

Universal Learning of Nonlinear Dynamics
por: Dogariu, Evan, et al.
Publicado: (2025)

Introduction to Online Control
por: Hazan, Elad, et al.
Publicado: (2022)

Online Control in Population Dynamics
por: Golowich, Noah, et al.
Publicado: (2024)

Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
por: Cai, Qi, et al.
Publicado: (2022)

Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks
por: Xu, Zhenghao, et al.
Publicado: (2024)

Provably-Stable Neural Network-Based Control of Nonlinear Systems
por: Li, Anran, et al.
Publicado: (2025)

Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts
por: Liao, Fangshuo, et al.
Publicado: (2025)

Sparse Graphical Linear Dynamical Systems
por: Chouzenoux, Emilie, et al.
Publicado: (2023)

Symmetric Linear Dynamical Systems are Learnable from Few Observations
por: Vu, Minh, et al.
Publicado: (2025)

Provably Convergent Federated Trilevel Learning
por: Jiao, Yang, et al.
Publicado: (2023)

Provably Efficient Exploration in Policy Optimization
por: Cai, Qi, et al.
Publicado: (2019)

Topology Learning of unknown Networked Linear Dynamical System excited by Cyclostationary inputs
por: Doddi, Harish, et al.
Publicado: (2020)

Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
por: Zhou, Quan, et al.
Publicado: (2020)

Learning Provably Improves the Convergence of Gradient Descent
por: Song, Qingyu, et al.
Publicado: (2025)

Muon is Provably Faster with Momentum Variance Reduction
por: Qian, Xun, et al.
Publicado: (2025)

Provable Mixed-Noise Learning with Flow-Matching
por: Hagemann, Paul, et al.
Publicado: (2025)

MGDA Converges under Generalized Smoothness, Provably
por: Zhang, Qi, et al.
Publicado: (2024)

Provably data-driven projection method for quadratic programming
por: Nguyen, Anh Tuan, et al.
Publicado: (2025)

Provable Adaptivity of Adam under Non-uniform Smoothness
por: Wang, Bohan, et al.
Publicado: (2022)

Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
por: Deng, Yuyang, et al.
Publicado: (2024)

On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
por: Sahu, Sharan, et al.
Publicado: (2026)

Provable Exactness for Asymmetric Low-Rank SDP Learning
por: Hu, Enliang
Publicado: (2018)

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning
por: Zhang, Thomas T., et al.
Publicado: (2025)

Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
por: Chu, Ya-Chi, et al.
Publicado: (2025)

Momentum Benefits Non-IID Federated Learning Simply and Provably
por: Cheng, Ziheng, et al.
Publicado: (2023)

Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements
por: Chae, Jiseok, et al.
Publicado: (2024)

Provable optimal transport with transformers: The essence of depth and prompt engineering
por: Daneshmand, Hadi
Publicado: (2024)

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods
por: Tao, Yuanzhe, et al.
Publicado: (2024)

Learning Linear Dynamics from Bilinear Observations
por: Sattar, Yahya, et al.
Publicado: (2024)

First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
por: Shulgin, Egor, et al.
Publicado: (2025)

Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach
por: Wang, Yao, et al.
Publicado: (2025)

Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
por: Palenzuela, Karlo, et al.
Publicado: (2025)

A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization
por: Chu, Tianshu, et al.
Publicado: (2025)

Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling
por: Li, Junyi, et al.
Publicado: (2024)

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
por: Tucat, Matteo, et al.
Publicado: (2024)

A Provably Convergent and Practical Algorithm for Gromov--Wasserstein Optimal Transport
por: Liang, Ling, et al.
Publicado: (2026)

Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics
por: Choi, Sunmook, et al.
Publicado: (2025)

Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
por: Vaswani, Sharan, et al.
Publicado: (2025)