Image de couverture de livre

Distributed adaptive estimation for stochastic large regression models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Gan, Die, Xie, Siyu, Liu, Zhixin, Zhang, Xuebo
Format:	Preprint
Publié:	2026
Sujets:	Systems and Control Optimization and Control Statistics Theory
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Stability analysis of distributed Kalman filtering algorithm for stochastic regression model
par: Xie, Siyu, et autres
Publié: (2024)

Constrained recursive kernel density/regression estimation by stochastic quasi-gradient methods
par: Norkin, Vladimir, et autres
Publié: (2024)

The optimizing mode classification stabilization of sampled stochastic jump systems via an improved hill-climbing algorithm based on Q-learning
par: Wang, Guoliang
Publié: (2024)

Unscented Trajectory Optimization
par: Ross, I. M., et autres
Publié: (2024)

Distributionally-Robust Optimization with Noisy Data for Discrete Uncertainties Using Total Variation Distance
par: Farokhi, Farhad
Publié: (2023)

Markov Kernels, Distances and Optimal Control: A Parable of Linear Quadratic Non-Gaussian Distribution Steering
par: Teter, Alexis M. H., et autres
Publié: (2025)

Evaluability of paired comparison data in stochastic paired comparison models: Necessary and sufficient condition
par: Gyarmati, László, et autres
Publié: (2025)

Entropic vector quantile regression: Duality and Gaussian case
par: Kato, Kengo, et autres
Publié: (2026)

Optimal distributions for randomized unbiased estimators with an infinite horizon and an adaptive algorithm
par: Zheng, Chao, et autres
Publié: (2023)

Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
par: Saldi, Naci, et autres
Publié: (2025)

Autonomy in the Real-World: Autonomous Trajectory Planning for Asteroid Reconnaissance via Stochastic Optimization
par: Echigo, Kazuya, et autres
Publié: (2024)

Joint Learning of Linear Dynamical Systems under Smoothness Constraints
par: Tyagi, Hemant
Publié: (2024)

Learning linear dynamical systems under convex constraints
par: Tyagi, Hemant, et autres
Publié: (2023)

Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
par: Lamperski, Andrew, et autres
Publié: (2024)

Distributional stability of sparse inverse covariance matrix estimators
par: Chen, Renjie, et autres
Publié: (2024)

Low-rank matrix recovery via nonconvex optimization methods with application to errors-in-variables matrix regression
par: Li, Xin, et autres
Publié: (2024)

Wasserstein Distributionally Robust Quantile Regression
par: Zhang, Chunxu, et autres
Publié: (2026)

Off-policy estimation with adaptively collected data: the power of online learning
par: Lee, Jeonghwan, et autres
Publié: (2024)

Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimization
par: Wang, Han, et autres
Publié: (2025)

The out-of-sample prediction error of the square-root-LASSO and related estimators
par: Olea, José Luis Montiel, et autres
Publié: (2022)

Sample Average Approximation for Distributionally Robust Optimization with $ϕ$-divergences
par: Li, Yan
Publié: (2026)

A Christoffel-like function for high-dimensional support inference in graphical models
par: Lasserre, Jean-Bernard, et autres
Publié: (2024)

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models
par: Mou, Wenlong
Publié: (2025)

Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources
par: Rychener, Yves, et autres
Publié: (2024)

Low-rank Tensor Autoregressive Predictor for Third-Order Time-Series Forecasting
par: Wang, Haoning, et autres
Publié: (2024)

Statistical Inference of Day-to-Day Traffic Dynamics
par: Wu, Minghui, et autres
Publié: (2026)

Sharp recovery and landscape guarantees for the nonconvex matrix LASSO
par: McRae, Andrew D., et autres
Publié: (2026)

Estimating the Convex Hull of the Image of a Set with Smooth Boundary: Error Bounds and Applications
par: Lew, Thomas, et autres
Publié: (2023)

Optimization via Strategic Law of Large Numbers
par: Chen, Xiaohong, et autres
Publié: (2024)

Failure of uniform laws of large numbers for subdifferentials and beyond
par: Tian, Lai, et autres
Publié: (2025)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
par: Cheng, Xiuyuan, et autres
Publié: (2023)

Constraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them
par: Nouwens, S. A. N., et autres
Publié: (2024)

Accuracy of the Ensemble Kalman Filter in the Near-Linear Setting
par: Calvello, Edoardo, et autres
Publié: (2024)

Optimal transport and Wasserstein distances for causal models
par: Cheridito, Patrick, et autres
Publié: (2023)

Pattern formation using an intrinsic optimal control approach
par: Li, Tianhao, et autres
Publié: (2025)

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
par: Yang, Zhuoran, et autres
Publié: (2020)

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
par: Vukovic, Manojlo, et autres
Publié: (2026)

Minimax-optimal Halpern iterations for Lipschitz maps
par: Bravo, Mario, et autres
Publié: (2026)

Stochastic Optimal Control with Side Information and Bayesian Learning
par: Milz, Johannes, et autres
Publié: (2026)

Time-sensitive anytime-valid testing
par: Clerico, Eugenio, et autres
Publié: (2026)