MLE-Bench Benchmark

Context:
- It can (typically) evaluate AI agents on tasks including model training, data preprocessing, and hyperparameter optimization.
- It can (often) test skills like experimental execution and result submission in a competitive environment.
- It can range from evaluating simple linear regression models to advanced deep learning architectures across various domains, such as natural language processing and computer vision.
- It can utilize public leaderboards to compare AI agents with expert human performance, establishing baselines for bronze, silver, and gold medals.
- It can employ various metrics, including area under the curve (AUC), mean squared error (MSE), and domain-specific loss functions to assess performance.
- It can highlight strengths and limitations in AI by measuring factors like resource utilization, scalability, and generalization ability.
- It can explore potential dataset contamination risks and implement strategies to mitigate overfitting.
- It can promote future research by open-sourcing the benchmark code, enabling further analysis of AI’s performance in machine learning engineering tasks.
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Example(s):
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Counter-Example(s):
- SWE-bench.
- MLPerf benchmarks, which target hardware efficiency and AI system performance but do not simulate end-to-end competitions.
See: MLPerf Benchmark, Kaggle Competitions, AI Model Evaluation, Machine Learning Engineering Tasks.

References