DBScholar

Back to papers

A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference

Summary: Statistically-aware end-to-end optimizer for ML inference that cascades feature computation via a cost-model to select high-value, low-cost features. Demonstrates up to 5x speedups with negligible accuracy loss; interactive Jupyter notebooks illustrate applicable workloads and usage. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12329
Venue
VLDB
Year
2020
Pagerank
7.415647e-05
Overall Rank
3,438 | 76.42%
DOI
10.14778/3415478.3415487

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kraft_vldb20,
        title = {{A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference}},
        author = {Kraft, Peter and Kang, Daniel and Narayanan, Deepak and Palkar, Shoumik and Bailis, Peter and Zaharia, Matei},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {2833--2836},
        doi = {10.14778/3415478.3415487},
        url = {https://doi.org/10.14778/3415478.3415487},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
2,316 Evaluating End-to-End Optimization for Data Analytics Applications in Weld 2018 VLDB 8.7596739e-05
Previous Page 1 / 1 Next

Semantically Similar Papers