1.5k citations · 1.7k across the 10 of their papers we have counts for
10 papers
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…
A* Sampling
Chris J. Maddison, Daniel Tarlow, Tom Minka
The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we show how sampling from a continuous distribution…
Detecting Parameter Symmetries in Probabilistic Models
Robert Nishihara, Thomas Minka, Daniel Tarlow
Probabilistic models often have parameters that can be translated, scaled, permuted, or otherwise transformed without changing the model. These symmetries can lead to strong correl…
Expectation Propagation for approximate Bayesian inference
Thomas P. Minka
This paper presents a new deterministic approximation technique in Bayesian networks. This method, "Expectation Propagation", unifies two previous techniques: assumed-density filte…
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…
Structured Region Graphs: Morphing EP into GBP
Max Welling, Thomas P. Minka, Yee Whye Teh
GBP and EP are two successful algorithms for approximate probabilistic inference, which are based on different approximation strategies. An open problem in both algorithms has been…