1 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
MR-Align: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models
Xinming Wang, Jian Xu, Bin Yu +9
Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation i…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
PCS Workflow for Veridical Data Science in the Age of AI
Zachary T. Rewolinski, Bin Yu
Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medi…
Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects
Juraj Bodik, Yaxuan Huang, Bin Yu
While average treatment effects (ATE) and conditional average treatment effects (CATE) provide valuable population- and subgroup-level summaries, they fail to capture uncertainty a…
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Ilia Azizi, Juraj Bodik, Jakob Heiss +1
Accurate uncertainty quantification is critical for reliable predictive modeling. Existing methods typically address either aleatoric uncertainty due to measurement noise or episte…
Statistics at a Crossroads; Who is for the Challenge?
Xuming He, David Madigan, Bin Yu +1
This project was sponsored by the National Science Foundation and organized by a steering committee and a group of theme leaders. The six-member steering committee, consisting of J…