MLBench: Benchmarking Machine Learning Services Against Human Experts
Summary: MLBench is a Kaggle-derived benchmark with raw features and winning-team features, establishing human-expert baselines for ML-service evaluation. It enables quantitative cloud-service comparisons (Amazon, Azure) via relative Kaggle rankings and best-effort accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yu Liu
- 2. Hantian Zhang
- 3. Luyuan Zeng
- 4. Wentao Wu
- 5. Ce Zhang
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,123 | Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-Precision Learning | 2019 | VLDB | 5.6796998e-05 |
| 5,605 | TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems | 2023 | VLDB | 5.4142007e-05 |
| 11,676 | doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases | 2019 | VLDB | 4.1945683e-05 |
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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 |
|---|---|---|---|---|
| 1,391 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads | 2018 | VLDB | 0.0001223506 |
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