182 citations · 225 across the 13 of their papers we have counts for
6 papers · 1 filter
Bayesian Inference for Correlated Human Experts and Classifiers
Markelle Kelly, Alex Boyd, Sam Showalter +2
Applications of machine learning often involve making predictions based on both model outputs and the opinions of human experts. In this context, we investigate the problem of quer…
Anomaly Detection of Tabular Data Using LLMs
Aodong Li, Yunhan Zhao, Chen Qiu +4
Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning. In this paper, we study the problem of using LLMs to detect tabula…
What Large Language Models Know and What People Think They Know
Mark Steyvers, Heliodoro Tejeda, Aakriti Kumar +5
As artificial intelligence (AI) systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their output…
Bayesian Online Learning for Consensus Prediction
Sam Showalter, Alex Boyd, Padhraic Smyth +1
Given a pre-trained classifier and multiple human experts, we investigate the task of online classification where model predictions are provided for free but querying humans incurs…
Zero-Shot Anomaly Detection via Batch Normalization
Aodong Li, Chen Qiu, Marius Kloft +3
Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The challenge of adapting an anomaly detector to drift in the normal data distribution, esp…
Deep Anomaly Detection under Labeling Budget Constraints
Aodong Li, Chen Qiu, Marius Kloft +3
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud…