17 citations · 19 across the 7 of their papers we have counts for
8 papers
Stance Drift: How AI-mediated Communication Distorts Our Message
Lingchong Liu, Yanfei Zhou, Jacob Bien +3
Large language models (LLMs) increasingly mediate human communication, from drafting emails to summarizing scientific reports, yet whether they faithfully preserve a speaker's posi…
Audited Conformal Prediction for Classification under Unknown Distribution Shift
Yanfei Zhou, Rizal Fathony, Nam H. Nguyen +1
We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift. We propose Audited Conformal Prediction (ACP)…
Are LLM Decisions Faithful to Verbal Confidence?
Jiawei Wang, Yanfei Zhou, Siddartha Devic +1
Large Language Models (LLMs) can produce surprisingly sophisticated estimates of their own uncertainty. However, it remains unclear to what extent this expressed confidence is tied…
Conformal Inference for Open-Set and Imbalanced Classification
Tianmin Xie, Yanfei Zhou, Ziyi Liang +2
This paper presents a conformal prediction method for classification in highly imbalanced and open-set settings, where there are many possible classes and not all may be represente…
Conformal Classification with Equalized Coverage for Adaptively Selected Groups
Yanfei Zhou, Matteo Sesia
This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen feat…
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
Yanfei Zhou, Lars Lindemann, Matteo Sesia
This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high proba…
Conformal inference is (almost) free for neural networks trained with early stopping
Ziyi Liang, Yanfei Zhou, Matteo Sesia
Early stopping based on hold-out data is a popular regularization technique designed to mitigate overfitting and increase the predictive accuracy of neural networks. Models trained…
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia +1
Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate re…