Underspecification Presents Challenges for Credibility in Modern Machine Learning.¶
D'Amour, A. (2022). Underspecification Presents Challenges for Credibility in Modern Machine Learning. Journal of Machine Learning Research, 23(226), 1-61.
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Primes¶
- Underspecification
- In machine learning, multiple models with identical validation performance encode different decision surfaces; on a stress test — a subgroup, a shifted input, an adversarial probe — they diverge sharply, and the choice among them was governed by initialization or training noise.
This sourceShows that models with identical held-out performance encode different decision surfaces and diverge sharply under stress tests, the closure being seed and training noise.
- In machine learning, multiple models with identical validation performance encode different decision surfaces; on a stress test — a subgroup, a shifted input, an adversarial probe — they diverge sharply, and the choice among them was governed by initialization or training noise.
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