From the 1 of 13 linked papers with an AI index.
13 papers
Tight Generalization Bound for AdaBoost
Mikael Møller Høgsgaard
The paper derives a tight upper bound on the generalization error of AdaBoost, expressed in terms of the weak learner's advantage, VC-dimension, sample size, and confidence level,…
Aggregation with Exponential Weights is Optimal in Expectation
Mikael Møller Høgsgaard, Patrick Rebeschini, Tobias Wegel
The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss. In particular, whether it is…
The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity
Mikael Møller Høgsgaard, Kasper Green Larsen, Liang-Yu Zou
This work investigates theoretically the interplay between interpolation and aggregation in regression. We establish that the -graph dimension characterizes learnability for a…
The Optimal Sample Complexity of Linear Contracts
Mikael Møller Høgsgaard
In this paper, we settle the problem of learning optimal linear contracts from data in the offline setting, where agent types are drawn from an unknown distribution and the princip…
Agnostic Language Identification and Generation
Mikael Møller Høgsgaard, Chirag Pabbaraju
Recent works on language identification and generation have established tight statistical rates at which these tasks can be achieved. These works typically operate under a strong r…
Revisiting Agnostic Boosting
Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice +1
Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones. While well studied in the realizable case, the statistical properties of w…