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

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

25 May 2023
Hilaf Hasson
Danielle C. Maddix
Yuyang Wang
Gaurav Gupta
Youngsuk Park
    UQCV
    AI4TS
    FedML
ArXivPDFHTML

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

3 / 3 papers shown
Title
A study on Ensemble Learning for Time Series Forecasting and the need
  for Meta-Learning
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning
J. Gastinger
S. Nicolas
Dusica Stepic
Mischa Schmidt
A. Schulke
AI4TS
56
16
0
23 Apr 2021
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson
Jonas W. Mueller
Alexander Shirkov
Hang Zhang
Pedro Larroy
Mu Li
Alex Smola
LMTD
84
607
0
13 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
270
5,660
0
05 Dec 2016
1