Official Tensorflow implementation for Deep Generative Positive-Unlabeled Learning under Selection Bias (VAE-PU) in CIKM 2020.

Longform data analysis article arguing every “dataset” is actually three: Observed (captured rows), Missing (what should exist but doesn’t), and Excluded (what filters/joins/dropna removed). Includes dataset accounting, join-loss and missingness audits, segmentation checks, and practical templates to prevent biased KPIs and wrong conclusions.

Code for a paper on estimation and evaluation of penalized survival models with high dimensional left-truncated and right-censored (LTRC) survival data

ELISL: Early-Late Synthetic Lethality Prediction in Cancer by Tepeli YI, Seale C, Gonçalves JP (bioRxiv 2022, Bioinformatics 2023)

Code accompanying the thesis project: "Understanding and Correcting Selection Bias in the Sentiments derived from Flemish Tweets".

An R Package for Estimation under Nonignorable Nonresponse in Sample Surveys

Simulated data for different selection bias structures

R code for reproduce real data analysis in MRAPSS paper.

Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning by Tepeli YI, de Wolf M, and Gonçalves JP (arXiv 2024)

SBSL: Selection Bias-resilient Synthetic Lethality prediction models by Seale CF, Tepeli YI, Gonçalves JP (Bioinformatics 2022)

DCAST: Diverse Class-Aware Self-Training for Fairer Learning by Tepeli YI and Gonçalves JP (arXiv 2024)

Bank marketing ML model (92% ROC-AUC) with XGBoost + Platt scaling. EDA-driven binning, handles 93% class imbalance, addresses data leakage & selection bias. 16 docs covering nuances & business impact.