Fitting models from noisy heuristic labels
Explains the 'data programming' weak supervision paradigm for training models using noisy heuristic labels, with a practical example.
Explains the 'data programming' weak supervision paradigm for training models using noisy heuristic labels, with a practical example.
A critique of publishing code as images in academic papers, highlighting errors and reproducibility issues in statistical computing examples.
Analyzes efficiency differences between weighted and unweighted logistic regression in case-control studies, showing when ignoring weights is beneficial.
A technical talk on using the bbmle package in R to perform Maximum Likelihood Estimation for fitting mechanistic ecological models.