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Auto-PyTorch tabular multi-fidelity AutoDL

Contribution grade

Automated machine learning software · Human-designed research method or resource

Reference score75,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combines architecture/training configuration search, BOHB multi-fidelity budgets, portfolio warmstarts and post-hoc ensembling for tabular automated learning.

Scientific scope

Combines architecture/training configuration search, BOHB multi-fidelity budgets, portfolio warmstarts and post-hoc ensembling for tabular automated learning. BOHB and ensemble-selection mechanisms are inherited, not newly invented. Performance concerns the specified configuration spaces and benchmarks, with search and training budgets; not a guaranteed best learner for arbitrary datasets.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. BOHB and ensemble-selection mechanisms are inherited, not newly invented. Performance concerns the specified configuration spaces and benchmarks, with search and training budgets; not a guaranteed best learner for arbitrary datasets.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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