Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the...
arXiv:2609.17296v1 Announce Type: cross
Abstract: Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public poli...
By Ying Jin, Naoki Egami
arXiv:2603. 14198v3 Announce Type: replace-cross Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification.
By Haifeng Wen, Osvaldo Simeone, Hong Xing
arXiv:2607. 17311v1 Announce Type: cross Abstract: The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems.
By Jovan Nikolic, Maciej Krzysztof Zuziak, Evangelos Pournaras
arXiv:2601. 17944v2 Announce Type: replace-cross Abstract: We study repeated allocation of shared resources among agents with time-varying demands and capped linear utilities.
By Seyed Majid Zahedi, Rupert Freeman
arXiv:2606. 00717v1 Announce Type: cross Abstract: Uncertainty quantification is essential in high-stakes machine learning tasks.
By Martin V. Vejling, Christophe A. N. Biscio, Adrien Mazoyer, Petar Popovski, Shashi Raj Pandey