A Note On Nonlinear Regression Under L2 Loss
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Abstract (arXiv)
We investigate the nonlinear regression problem under L2 loss (square loss) functions. Traditional nonlinear regression models often result in non-convex optimization problems with respect to the parameter set. We show that a convex nonlinear regression model exists for the traditional least squares problem, which can be a promising towards designing more complex systems with easier to train models.
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References · 7
- 2021Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statisticscited by 32
- 2021Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clusteringcited by 12
- 2022Low Regret Binary Sampling Method for Efficient Global Optimization of Univariate Functionscited by 11
- 2021Optimal and Efficient Algorithms for General Mixable Losses against Switching Oraclescited by 10
- 2022Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statisticscited by 8
- 2021Efficient, Anytime Algorithms for Calibration with Isotonic Regression under Strictly Convex Lossescited by 5
- 2022Efficient Minimax Optimal Global Optimization of Lipschitz Continuous Multivariate Functionscited by 2
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