Accelerating Generalized Linear Models by Trading off Computation for Uncertainty
CoRR(2023)
摘要
Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic
framework to model categorical, ordinal and continuous data, and are widely
used in practice. However, exact inference in GLMs is prohibitively expensive
for large datasets, thus requiring approximations in practice. The resulting
approximation error adversely impacts the reliability of the model and is not
accounted for in the uncertainty of the prediction. In this work, we introduce
a family of iterative methods that explicitly model this error. They are
uniquely suited to parallel modern computing hardware, efficiently recycle
computations, and compress information to reduce both the time and memory
requirements for GLMs. As we demonstrate on a realistically large
classification problem, our method significantly accelerates training compared
to competitive baselines by trading off reduced computation for increased
uncertainty.
更多查看译文
关键词
generalized linear models,uncertainty,computation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要