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Margin-Based Generalization Lower Bounds for Boosted Classifiers
Allan Grønlund · Lior Kamma · Kasper Green Larsen · Alexander Mathiasen · Jelani Nelson

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #8
Boosting is one of the most successful ideas in machine learning. The most well-accepted explanations for the low generalization error of boosting algorithms such as AdaBoost stem from margin theory. The study of margins in the context of boosting algorithms was initiated by Schapire, Freund, Bartlett and Lee (1998), and has inspired numerous boosting algorithms and generalization bounds. To date, the strongest known generalization (upper bound) is the $k$th margin bound of Gao and Zhou (2013). Despite the numerous generalization upper bounds that have been proved over the last two decades, nothing is known about the tightness of these bounds. In this paper, we give the first margin-based lower bounds on the generalization error of boosted classifiers. Our lower bounds nearly match the $k$th margin bound and thus almost settle the generalization performance of boosted classifiers in terms of margins.

Author Information

Allan Grønlund (Aarhus University, MADALGO)
Lior Kamma (Aarhus University)
Kasper Green Larsen (Aarhus University)
Alexander Mathiasen (Aarhus University)
Jelani Nelson (UC Berkeley)

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