12 citations · 21 across the 10 of their papers we have counts for
5 papers · 1 filter
Synthetic continued pretraining
Zitong Yang, Neil Band, Shuangping Li +2
Pretraining on large-scale, unstructured internet text enables language models to acquire a significant amount of world knowledge. However, this knowledge acquisition is data-ineff…
Can Unconfident LLM Annotations Be Used for Confident Conclusions?
Kristina Gligorić, Tijana Zrnic, Cinoo Lee +2
Large language models (LLMs) have shown high agreement with human raters across a variety of tasks, demonstrating potential to ease the challenges of human data collection. In comp…
Active Statistical Inference
Tijana Zrnic, Emmanuel J. Candès
Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection. Assu…
A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features
Emi Zeger, Yifei Wang, Aaron Mishkin +3
We prove that training neural networks on 1-D data is equivalent to solving convex Lasso problems with discrete, explicitly defined dictionary matrices. We consider neural networks…
Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series
Zitong Yang, Emmanuel Candès, Yu Ming Huang +1
We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides calibrated prediction intervals. Unlike the existing m…