Cover Image

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hasson, Hilaf, Maddix, Danielle C., Wang, Yuyang, Gupta, Gaurav, Park, Youngsuk
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Theoretical Guarantees for the Subspace-Constrained Tyler's Estimator
by: Lerman, Gilad, et al.
Published: (2024)

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
by: Neglia, Roberto, et al.
Published: (2025)

Sparse Tucker Decomposition and Graph Regularization for High-Dimensional Time Series Forecasting
by: Xia, Sijia, et al.
Published: (2026)

Information-Theoretic Guarantees for Recovering Low-Rank Tensors from Symmetric Rank-One Measurements
by: Kızıldağ, Eren C.
Published: (2025)

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
by: Masserano, Luca, et al.
Published: (2024)

Multi-Step Consistency Models: Fast Generation with Theoretical Guarantees
by: Jain, Nishant, et al.
Published: (2025)

Nonlinear Meta-Learning Can Guarantee Faster Rates
by: Meunier, Dimitri, et al.
Published: (2023)

Statistical Learning Guarantees for Group-Invariant Barron Functions
by: Yang, Yahong, et al.
Published: (2025)

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
by: Wang, Kevin, et al.
Published: (2026)

Small Ensemble-based Data Assimilation: A Machine Learning-Enhanced Data Assimilation Method with Limited Ensemble Size
by: Li, Zhilin, et al.
Published: (2025)

Sparsified-Learning for High-Dimensional Heavy-Tailed Locally Stationary Time Series, Concentration and Oracle Inequalities
by: Wang, Yingjie, et al.
Published: (2025)

Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
by: de Souza, Douglas Baptista, et al.
Published: (2024)

Topological Analysis for Detecting Anomalies (TADA) in Time Series
by: Chazal, Frédéric, et al.
Published: (2024)

Frequentist Guarantees of Distributed (Non)-Bayesian Inference
by: Wu, Bohan, et al.
Published: (2023)

Cloning is as Hard as Learning for Stabilizer States
by: Bansal, Nikhil, et al.
Published: (2026)

Kernel-based Optimally Weighted Conformal Time-Series Prediction
by: Lee, Jonghyeok, et al.
Published: (2024)

Bounds in Wasserstein Distance for Locally Stationary Functional Time Series
by: Tinio, Jan Nino G., et al.
Published: (2025)

Benign Overfitting in Time Series Linear Models with Over-Parameterization
by: Nakakita, Shogo, et al.
Published: (2022)

Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
by: Peng, Liangzu, et al.
Published: (2026)

Node-Level Uncertainty Estimation in LLM-Generated SQL
by: Hasson, Hilaf, et al.
Published: (2025)

A Unified Information-Theoretic Framework for Meta-Learning Generalization
by: Wen, Wen, et al.
Published: (2025)

Improved Guarantees for Langevin Monte Carlo with Average Smoothness
by: Dalalyan, Arnak S., et al.
Published: (2026)

Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks
by: Taheri, Mahsa, et al.
Published: (2022)

Sequential Bayesian Neural Subnetwork Ensembles
by: Jantre, Sanket, et al.
Published: (2022)

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
by: Altekrüger, Fabian, et al.
Published: (2023)

KL Convergence Guarantees for Score diffusion models under minimal data assumptions
by: Conforti, Giovanni, et al.
Published: (2023)

Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
by: Dmitriev, Daniil, et al.
Published: (2026)

On the Nonasymptotic Scaling Guarantee of Hyperparameter Estimation in Inhomogeneous, Weakly-Dependent Complex Network Dynamical Systems
by: Yu, Yi, et al.
Published: (2026)

Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees
by: Fan, Jianqing, et al.
Published: (2025)

$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
by: Fujisawa, Masahiro, et al.
Published: (2025)

Stronger Neyman Regret Guarantees for Adaptive Experimental Design
by: Noarov, Georgy, et al.
Published: (2025)

Multi-period Learning for Financial Time Series Forecasting
by: Zhang, Xu, et al.
Published: (2025)

Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction
by: Tang, Yin, et al.
Published: (2024)

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators
by: Wang, Longlin, et al.
Published: (2025)

Conformal Prediction with Time-Series Data via Sequential Conformalized Density Regions
by: Sampson, M., et al.
Published: (2026)

Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
by: Jiang, Hanyang, et al.
Published: (2026)

Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression
by: Kim, Yeichan, et al.
Published: (2025)

Bayesian Models for Joint Selection of Features and Auto-Regressive Lags: Theory and Applications in Environmental and Financial Forecasting
by: Manna, Alokesh, et al.
Published: (2025)

Empirical Likelihood for Random Forests and Ensembles
by: Chiang, Harold D., et al.
Published: (2025)

Diffusion Models with Heavy-Tailed Targets: Score Estimation and Sampling Guarantees
by: Yu, Yifeng, et al.
Published: (2026)