4 papers
Some aspects of robustness in modern Markov Chain Monte Carlo
Sam Power, Giorgos Vasdekis
Markov Chain Monte Carlo (MCMC) is a flexible approach to approximate sampling from intractable probability distributions, with a rich theoretical foundation and comprising a wealt…
Distributional Training Data Attribution: What do Influence Functions Sample?
Bruno Mlodozeniec, Isaac Reid, Sam Power +4
Randomness is an unavoidable part of training deep learning models, yet something that traditional training data attribution algorithms fail to rigorously account for. They ignore…
Analysis of Multiple-try Metropolis via Poincaré inequalities
Rocco Caprio, Sam Power, Andi Q. Wang
We study the Multiple-try Metropolis algorithm using the framework of Poincaré inequalities. We describe the Multiple-try Metropolis as an auxiliary variable implementation of a re…
A New Proof of Sub-Gaussian Norm Concentration Inequality
Zishun Liu, Sam Power, Yongxin Chen
We present a new method for proving the norm concentration inequality of sub-Gaussian variables. Our proof is based on an averaged version of the moment generating function, termed…