MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
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4 Pith papers cite this work. Polarity classification is still indexing.
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ParamBoost improves GAMs by fitting piecewise cubic polynomials via gradient boosting and supports constraints for continuity, monotonicity, convexity, and feature interactions.
A computationally efficient three-step marginal method for longitudinal function-on-function regression that fits pointwise scalar-on-function models, smooths along the bivariate domain, and derives confidence bands to enable valid inference on large functional datasets.
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Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
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ParamBoost: Gradient Boosted Piecewise Cubic Polynomials
ParamBoost improves GAMs by fitting piecewise cubic polynomials via gradient boosting and supports constraints for continuity, monotonicity, convexity, and feature interactions.
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Efficient Longitudinal Function-on-Function Regression
A computationally efficient three-step marginal method for longitudinal function-on-function regression that fits pointwise scalar-on-function models, smooths along the bivariate domain, and derives confidence bands to enable valid inference on large functional datasets.
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