2022
AISTATS
AISTATS 2022
Sampling from Arbitrary Functions via PSD Models
Abstract
In many areas of applied statistics and machine learning, generating an arbitrary number of inde- pendent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through evaluations of the density, current methods either scale badly with the dimension or require very involved implemen- tations. Instead, we take a two-step approach by first modeling the probability distribution and then sampling from that model. We use the recently introduced class of positive semi-definite (PSD) models which have been shown to be e
🌉
Interdisciplinary Bridge
- Machine Learning and Mathematics & Optimization
🧭
Keyword Pioneer
- positive semi-definite model
🐝
Cross-Pollinator
- Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Stochastic Processes
Data Science & Analytics > Methods > Data Mining
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Mathematics & Optimization > Probability > Stochastic Processes