Name
Abstract | ||
|---|---|---|
Stochastic gradient Langevin dynamics (SGLD) is a widely used sampler for the posterior inference with a large scale dataset. Although SGLD is designed for unbounded random variables, many practical models incorporate variables with boundaries such as non-negative ones or those in a finite interval. Existing modifications of SGLD for handling bounded random variables resort to heuristics without a formal guarantee of sampling from the true stationary distribution. In this paper, we reformulate the SGLD algorithm incorporating a deterministic transformation with rigorous theories. Our method transforms unbounded samples obtained by SGLD into the domain of interest. We demonstrate transformed SGLD in both artificial problem settings and real-world applications of Bayesian non-negative matrix factorization and binary neural networks. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Machine Learning | Journal |
Volume | Citations | PageRank |
abs/1903.02750 | 0 | 0.34 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soma Yokoi | 1 | 0 | 0.34 |
| Takuma Otsuka | 2 | 0 | 0.34 |
| Issei Sato | 3 | 331 | 41.59 |