arXiv:2609.23926v1 Announce Type: cross
Abstract: Density-Ratio Rescoring (DRR) augments a classifier trained at the original class prior with a survey-raking dual score. Raking reweights the majorit...
By Dongha Kim, Seunghwan Park
Density‑Ratio Rescoring (DRR) enhances a classifier trained with the original class prior by adding a survey‑raking dual score that reweights the majority class to match minority feature moments within a tolerance. The method standardizes both the dual and base scores, combines them with equal weight, and uses the fitted dual directly for prediction without resampling or refitting the base model. Experiments on 24 tabular benchmarks and a gene‑expression cohort show that DRR improves average precision over the standardized base on every dataset, with a mean gain of 0.034, and outperforms a shared‑dual raking‑and‑relabeling resampler on most datasets.
By Dongha Kim, Seunghwan Park
arXiv:2608. 19748v1 Announce Type: cross Abstract: Inference-time selection methods, such as Best-of-N, improve generation by sampling a pool of candidates and selecting the top-ranked completion according to a reward model.
By Yarin Bar, Yaniv Romano
arXiv:2607. 22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks.
By Yi-Hang Zhu, Rajeev Raman, Shiqi Su, Jianyuan Sun, Xinyu Yang, Nan Xing, Huiyu Zhou
arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.
By Rostislav Gusev, Alexey Zaytsev
arXiv:2608.30699v1 Announce Type: cross
Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...
By Yue Cheng, Jiajun Zhang, Xiaohui Gao, Weiwei Xing, Zhanxing Zhu