Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Asynchronous Distributed Variational Gaussian Process for Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use asynchronous distributed variational inference instead of synchronous DistGP-GD | ADVGP · US Flight 700K train/100K test | RMSE: 32.9411 -> 32.9080; 32.8069 -> 32.7543; 32.6521 -> 32.6143 | Helped |
| Use asynchronous distributed variational inference instead of synchronous DistGP-GD | ADVGP · US Flight 2M train/100K test | RMSE: 36.0142 -> 36.1156; 35.9487 -> 35.8347; 35.7971 -> 35.7017 | Mixed |
| Use GP regression instead of linear regression | ADVGP · NYC Taxi 100M train/500K test | RMSE: 424.8 -> 333.4 | Helped |
| Use GP regression instead of linear regression | ADVGP · NYC Taxi 1B train/1M test | RMSE: 362.8 -> 309.7 | Helped |
| Increase the delay limit from synchronous operation, then tune it to avoid excessive asynchrony (ablation) | ADVGP · US Flight data with simulated worker latency | RMSE | Mixed |
| Use asynchronous computation rather than synchronous coordination | ADVGP · US Flight scalability test | per-iteration time | Helped |
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