Name
Abstract | ||
|---|---|---|
The ubiquitous use of machine learning algorithms brings new challenges to traditional database problems such as incremental view update. Much effort is being put in better understanding and debugging machine learning models, as well as in identifying and repairing errors in training datasets. Our focus is on how to assist these activities when they have to retrain the machine learning model after removing problematic training samples in cleaning or selecting different subsets of training data for interpretability. This paper presents an efficient provenance-based approach, PrIU, and its optimized version, PrIU-opt, for incrementally updating model parameters without sacrificing prediction accuracy. We prove the correctness and convergence of the incrementally updated model parameters, and validate it experimentally. Experimental results show that up to two orders of magnitude speed-ups can be achieved by PrIU-opt compared to simply retraining the model from scratch, yet obtaining highly similar models.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3318464.3380571 | SIGMOD/PODS '20: International Conference on Management of Data
Portland
OR
USA
June, 2020 |
Keywords | DocType | ISBN |
Data provenance, machine learning, deletion propagation | Conference | 978-1-4503-6735-6 |
Citations | PageRank | References |
0 | 0.34 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinjun Wu | 1 | 11 | 4.39 |
| Val Tannen | 2 | 2367 | 518.95 |
| Susan Davidson | 3 | 3076 | 1016.56 |