Tootfinder

Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
Gjorgjina Cenikj, Ga\v{s}per Petelin, Tome Eftimov
https://arxiv.org/abs/2507.01668

Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
The field of numerical optimization has recently seen a surge in the development of "novel" metaheuristic algorithms, inspired by metaphors derived from natural or human-made processes, which have been widely criticized for obscuring meaningful innovations and failing to distinguish themselves from existing approaches. Aiming to address these concerns, we investigate the applicability of statistical tests for comparing algorithms based on their search behavior. We utilize the cross-match statis…

This https://arxiv.org/abs/2404.17589 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems
As the last critical stage of RSs, Multi-Task Fusion (MTF) is responsible for combining multiple scores outputted by Multi-Task Learning (MTL) into a final score to maximize user satisfaction, which determines the ultimate recommendation results. Recently, to optimize long-term user satisfaction within a recommendation session, Reinforcement Learning (RL) is used for MTF in the industry. However, the offline RL algorithms used for MTF so far have the following severe problems: 1) to avoid out-o…