no code implementations • 11 Apr 2023 • Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan
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.
no code implementations • AKBC 2021 • John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Mishkov, Alex Spengler, Denise Wilkins, Sian Lindley, Richard Banks, Sam Webster, Yordan Zaykov
The knowledge discovery process uses a probabilistic program defining the process of generating the data item from a set of unknown typed entities.
2 code implementations • NIPS 2007 • Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel
We extend the Bayesian skill rating system TrueSkill to infer entire time series of skills of players by smoothing through time instead of filtering.
no code implementations • NeurIPS 2014 • Chris J. Maddison, Daniel Tarlow, Tom Minka
The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem.
no code implementations • NeurIPS 2011 • David A. Knowles, Tom Minka
Variational Message Passing (VMP) is an algorithmic implementation of the Variational Bayes (VB) method which applies only in the special case of conjugate exponential family models.
no code implementations • NeurIPS 2008 • Tom Minka, John Winn
We present general equations for expectation propagation and variational message passing in the presence of gates.