5 citations · 5 across the 5 of their papers we have counts for
3 papers · 1 filter
On the Effect of Regularization on Nonparametric Mean-Variance Regression
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin +1
Uncertainty quantification is vital for decision-making and risk assessment in machine learning. Mean-variance regression models, which predict both a mean and residual noise for e…
JANET: Joint Adaptive predictioN-region Estimation for Time-series
Eshant English, Eliot Wong-Toi, Matteo Fontana +3
Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicabil…
Understanding Pathologies of Deep Heteroskedastic Regression
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin +1
Deep, overparameterized regression models are notorious for their tendency to overfit. This problem is exacerbated in heteroskedastic models, which predict both mean and residual n…