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Poster
Model Similarity Mitigates Test Set Overuse
Horia Mania · John Miller · Ludwig Schmidt · Moritz Hardt · Benjamin Recht

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #3
in Algorithms -- Adaptive Data Analysis »

Excessive reuse of test data has become commonplace in today's machine learning workflows. Popular benchmarks, competitions, industrial scale tuning, among other applications, all involve test data reuse beyond guidance by statistical confidence bounds. Nonetheless, recent replication studies give evidence that popular benchmarks continue to support progress despite years of extensive reuse. We proffer a new explanation for the apparent longevity of test data: Many proposed models are similar in their predictions and we prove that this similarity mitigates overfitting. Specifically, we show empirically that models proposed for the ImageNet ILSVRC benchmark agree in their predictions well beyond what we can conclude from their accuracy levels alone. Likewise, models created by large scale hyperparameter search enjoy high levels of similarity. Motivated by these empirical observations, we give a non-asymptotic generalization bound that takes similarity into account, leading to meaningful confidence bounds in practical settings.

Keywords: Learning Theory · Adaptive Data Analysis · Algorithms · Theory
[ Paper]  [ Poster] 

Author Information

Horia Mania (UC Berkeley)
John Miller (University of California, Berkeley)
Ludwig Schmidt (UC Berkeley)
Moritz Hardt (University of California, Berkeley)
Benjamin Recht (UC Berkeley)

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