3 papers
cs.DL2026
Allocate Marginal Reviews to Borderline Papers Using LLM Comparative Ranking
Elliot L. Epstein, Rajat Dwaraknath, John Winnicki +1
This paper argues that large ML conferences should allocate marginal review capacity primarily to papers near the acceptance boundary, rather than spreading extra reviews via rando…
stat.ME2025
LLMs are Overconfident: Evaluating Confidence Interval Calibration with FermiEval
Elliot L. Epstein, John Winnicki, Thanawat Sornwanee +1
Large language models (LLMs) excel at numerical estimation but struggle to correctly quantify uncertainty. We study how well LLMs construct confidence intervals around their own an…
cs.LG2025
SD-KDE: Score-Debiased Kernel Density Estimation
Elliot L. Epstein, Rajat Dwaraknath, Thanawat Sornwanee +2
We propose a novel method for density estimation that leverages an estimated score function to debias kernel density estimation (SD-KDE). In our approach, each data point is adjust…