#risk estimation
2 resultscs.LG2026
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
Yushi Hirose, Hiroo Irobe, Takafumi Kanamori
The paper introduces a generalized, distribution‑free framework for semi‑supervised learning that builds unbiased risk estimators for both binary and multiclass problems, achieving…
#semi-supervised learning#distribution-free learning#multiclass classification#risk estimation
cs.LG2026
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
Xutao Wang, Hanting Chen, Tianyu Guo +1
The paper proposes PUe, a framework that improves positive‑unlabeled (PU) learning under biased label selection by using normalized propensity scores and inverse probability weight…
#positive-unlabeled learning#selection bias#propensity scoring#causal inference