Name
Title | ||
|---|---|---|
Noise Contrastive Meta-Learning For Conditional Density Estimation Using Kernel Mean Embeddings |
Abstract | ||
|---|---|---|
Current meta-learning approaches focus on learning functional representations of relationships between variables, i.e. estimating conditional expectations in regression. In many applications, however, the conditional distributions cannot be meaningfully summarized solely by expectation (due to e.g. multimodality). We introduce a novel technique for meta-learning conditional densities, which combines neural representation and noise contrastive estimation together with well-established literature in conditional mean embeddings into reproducing kernel Hilbert spaces. The method shows significant improvements over standard density estimation methods on synthetic and real-world data, by leveraging shared representations across multiple conditional density estimation tasks. |
Year | Venue | DocType |
|---|---|---|
2019 | 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS) | Journal |
Volume | ISSN | Citations |
130 | 2640-3498 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-Francois Ton | 1 | 0 | 3.38 |
| Lucian Chan | 2 | 0 | 0.68 |
| Yee Whye Teh | 3 | 6253 | 539.26 |
| Dino Sejdinovic | 4 | 443 | 37.96 |