Name
Title | ||
|---|---|---|
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. |
Abstract | ||
|---|---|---|
Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximations. We showcase the usefulness of the proposed method, its equivalence to constrained Bayesian variational inference and demonstrate its superiority over existing approaches in two applications, namely, fast log determinant estimation and information-theoretic Bayesian optimisation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/e21060551 | ENTROPY |
Keywords | Field | DocType |
maximum entropy,log determinant estimation,Bayesian optimisation | Mathematical optimization,Inference,Maximum entropy method,Equivalence (measure theory),Artificial intelligence,Principle of maximum entropy,Mathematics,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
21 | 6 | MEMe: An Accurate Maximum Entropy Method for Efficient
Approximations in Large-Scale Machine Learning. Entropy, 21(6), 551 (2019) |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diego Granziol | 1 | 6 | 2.15 |
| Bin Xin Ru | 2 | 1 | 4.42 |
| Stefan Zohren | 3 | 0 | 0.34 |
| Xiaowen Dong | 4 | 249 | 22.07 |
| Michael Osborne | 5 | 250 | 33.49 |
| stephen j roberts | 6 | 1244 | 174.70 |