3 papers in the archive (2026), average empirical rigor 7.8/10, average math complexity 7.5/10, 2 with linked code. Quadrant mix: Holy Grail ×3.
Works mostly in Machine Learning for Market Forecasting, Crypto Markets & DeFi.
Frequent co-authors: Agus Sudjianto (2).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Candidate-scoring decision models return a probability distribution over a presented set of answers. Public documentation of one such system, Jev, and of an open counterpart, JevAny, describes three training ingredients that determine what these probabilities estimate: soft targets from larger model
Traditional weight of evidence assigns a credit characteristic a value based on its association with default across a population. Credit analysis also requires an assessment of that characteristic in the context of an individual applicant’s record. We study conditional weight of evidence for this pu
A model pretrained to predict a customer’s next event can estimate the probability of future events without training on their labels and provide a reusable representation for supervised downstream banking tasks. We pretrain EventGPT, a hierarchical causal transformer, by next-token maximum likelihoo