124 citations · 238 across the 34 of their papers we have counts for
8 papers · 1 filter
Human-in-the-Loop Mixup
Katherine M. Collins, Umang Bhatt, Weiyang Liu +4
Aligning model representations to humans has been found to improve robustness and generalization. However, such methods often focus on standard observational data. Synthetic data i…
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Yuxin Xiao, Paul Pu Liang, Umang Bhatt +3
Pre-trained language models (PLMs) have gained increasing popularity due to their compelling prediction performance in diverse natural language processing (NLP) tasks. When formula…
Iterative Teaching by Data Hallucination
Zeju Qiu, Weiyang Liu, Tim Z. Xiao +5
We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of…
Eliciting and Learning with Soft Labels from Every Annotator
Katherine M. Collins, Umang Bhatt, Adrian Weller
The labels used to train machine learning (ML) models are of paramount importance. Typically for ML classification tasks, datasets contain hard labels, yet learning using soft labe…
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa +7
When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low…
On the Utility of Prediction Sets in Human-AI Teams
Varun Babbar, Umang Bhatt, Adrian Weller
Research on human-AI teams usually provides experts with a single label, which ignores the uncertainty in a model's recommendation. Conformal prediction (CP) is a well established…