12 citations · 21 across the 10 of their papers we have counts for
12 papers
Towards Execution-Grounded Automated AI Research
Chenglei Si, Zitong Yang, Yejin Choi +3
Automated AI research holds great potential to accelerate scientific discovery. However, current LLMs often generate plausible-looking but ineffective ideas. Execution grounding ma…
Robust Sampling for Active Statistical Inference
Puheng Li, Tijana Zrnic, Emmanuel Candès
Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming…
Imputation-Powered Inference
Sarah Zhao, Emmanuel Candès
Modern multi-modal and multi-site data frequently suffer from blockwise missingness, where subsets of features are missing for groups of individuals, creating complex patterns that…
Synthetic bootstrapped pretraining
Zitong Yang, Aonan Zhang, Hong Liu +4
We introduce Synthetic Bootstrapped Pretraining (SBP), a language model (LM) pretraining procedure that first learns a model of relations between documents from the pretraining dat…
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…
Probably Approximately Correct Labels
Emmanuel J. Candès, Andrew Ilyas, Tijana Zrnic
Obtaining high-quality labeled datasets is often costly, requiring either human annotation or expensive experiments. In theory, powerful pre-trained AI models provide an opportunit…
s1: Simple test-time scaling
Niklas Muennighoff, Zitong Yang, Weijia Shi +7
Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability but…
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…