The paper studies how to predict the winner of a district‑based election using as few queries to individual votes as possible. It introduces an adaptive randomized algorithm that, for any number of candidates and any error tolerance, correctly predicts the overall winner with probability at least 1‑δ using ≥O(1/ε² log m/δ log 1/δ) queries. This improves previous results for two candidates and balanced districts, and nearly matches a proven lower bound for constant‑size candidate sets.
By Koustav De, Debajyoti Kar, Swagato Sanyal
We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the predictor class.
arXiv:2602. 23128v2 Announce Type: replace Abstract: Generalization bounds for deep learning models are typically vacuous, not computable or restricted to specific model classes.
By Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain
arXiv:2607. 26838v1 Announce Type: new Abstract: In this paper we show that the generalization error of AdaBoost is $\Theta\big(\tfrac{d\ln(n\gamma^{2}/d)}{n\gamma^2}+\tfrac{\ln(1/\delta)}{n}\big)$, where $\gamma$ is the advantage guaranteed by the weak learner, $d$ is the VC-dimension of the class containing the weak hypotheses, $n$ is the sample size, and $\delta$ is the confidence parameter.
By Mikael M{\o}ller H{\o}gsgaard
arXiv:2606. 19105v1 Announce Type: new Abstract: We study PAC-Bayes derandomization for smooth loss functions.
By Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe Gigu\`ere
arXiv:2606. 08098v1 Announce Type: new Abstract: Majority voting over sampled answers is the dominant unsupervised aggregator for multi-sample LLM inference.
By Yasushi Sakai, Allen Song, Kent Larson