Search Results for author:
Found 11 papers, 5 papers with code
The Calibration Gap between Model and Human Confidence in Large Language Models
Recent work has focused on the quality of internal LLM confidence assessments, but the question remains of how well LLMs can communicate this internal model confidence to human users.
Bayesian Online Learning for Consensus Prediction
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 a cost.
Capturing Humans' Mental Models of AI: An Item Response Theory Approach
Improving our understanding of how humans perceive AI teammates is an important foundation for our general understanding of human-AI teams.
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration
An increasingly common use case for machine learning models is augmenting the abilities of human decision makers.
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
We investigate the problem of reliably assessing group fairness when labeled examples are few but unlabeled examples are plentiful.
Active Bayesian Assessment for Black-Box Classifiers
Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications.
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
We study the problem of estimating continuous quantities, such as prices, probabilities, and point spreads, using a crowdsourcing approach.
The Author-Topic Model for Authors and Documents
A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors.
Learning concept graphs from text with stick-breaking priors
We describe a generative model that is based on a stick-breaking process for graphs, and a Markov Chain Monte Carlo inference procedure.
The Wisdom of Crowds in the Recollection of Order Information
When individuals independently recollect events or retrieve facts from memory, how can we aggregate these retrieved memories to reconstruct the actual set of events or facts?
