41 citations · 143 across the 39 of their papers we have counts for
5 papers · 1 filter
Adaptive Mean Estimation by In-Context Learning: A Gradient-Flow Analysis
Martin Eppert, Krishna Balasubramanian, Subhro Ghosh +2
Prior Fitted Networks (PFNs) such as TabPFN now rival established statistical procedures across prediction and estimation tasks. A natural explanation is that PFNs have the propert…
Statistical attribute alignment for black-box generative AI via output post-processing
Kevin Jiang, Morgane Austern, Edgar Dobriban +1
Generative AI systems are increasingly used, but aligning their outputs with user requirements poses a continuing challenge. Here, we aim to ensure that the distribution of an attr…
High-Dimensional Statistics: Reflections on Progress and Open Problems
Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan +9
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced th…
Coupled Training with Privileged Information and Unlabeled Data
Jiahao Shi, Omar Hagrass, Jason M. Klusowski
In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model i…
Classification Imbalance as Transfer Learning
Eric Xia, Jason M. Klusowski
Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distr…