11 citations · 29 across the 6 of their papers we have counts for
6 papers
LSEMINK: A Modified Newton-Krylov Method for Log-Sum-Exp Minimization
Kelvin Kan, James G. Nagy, Lars Ruthotto
This paper introduces LSEMINK, an effective modified Newton-Krylov algorithm geared toward minimizing the log-sum-exp function for a linear model. Problems of this kind arise commo…
Multivariate Quantile Function Forecaster
Kelvin Kan, François-Xavier Aubet, Tim Januschowski +4
We propose Multivariate Quantile Function Forecaster (MQF), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its app…
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Youngsuk Park, Danielle Maddix, François-Xavier Aubet +3
Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint lear…
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
Kelvin Kan, James G Nagy, Lars Ruthotto
We demonstrate the ability of hybrid regularization methods to automatically avoid the double descent phenomenon arising in the training of random feature models (RFM). The hallmar…
PNKH-B: A Projected Newton-Krylov Method for Large-Scale Bound-Constrained Optimization
Kelvin Kan, Samy Wu Fung, Lars Ruthotto
We present PNKH-B, a projected Newton-Krylov method for iteratively solving large-scale optimization problems with bound constraints. PNKH-B is geared toward situations in which fu…
A two-stage method for spectral-spatial classification of hyperspectral images
Raymond H. Chan, Kelvin K. Kan, Mila Nikolova +1
This paper proposes a novel two-stage method for the classification of hyperspectral images. Pixel-wise classifiers, such as the classical support vector machine (SVM), consider sp…