1.5k citations · 1.7k across the 10 of their papers we have counts for
6 papers · 1 filter
Understanding Causality with Large Language Models: Feasibility and Opportunities
Cheng Zhang, Stefan Bauer, Paul Bennett +8
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question. We believe that…
Expectation-Propogation for the Generative Aspect Model
Thomas P. Minka, John Lafferty
The generative aspect model is an extension of the multinomial model for text that allows word probabilities to vary stochastically across documents. Previous results with aspect m…
How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Yoram Bachrach, Thore Graepel, Tom Minka +1
We propose a new probabilistic graphical model that jointly models the difficulties of questions, the abilities of participants and the correct answers to questions in aptitude tes…
Virtual Vector Machine for Bayesian Online Classification
Thomas P. Minka, Rongjing Xiang, Yuan +1
In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's…
Sparse-posterior Gaussian Processes for general likelihoods
Yuan, Qi, Ahmed H. Abdel-Gawad +1
Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs…
Variable sigma Gaussian processes: An expectation propagation perspective
Yuan Qi, Ahmed H. Abdel-Gawad, Thomas P. Minka
Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs…