1 citations · 4 across the 17 of their papers we have counts for
6 papers · 1 filter
Reconciling Universal and Uniform Learning with -Aggregation
Mikael Møller Høgsgaard, Patrick Rebeschini, Tobias Wegel
We study regression under bounded responses in terms of excess mean squared error. When the comparator class is finite, this setting is known as model selection aggregation, and ac…
Tight Generalization Bound for AdaBoost
Mikael Møller Høgsgaard
In this paper we show that the generalization error of AdaBoost is , where is the advantage guaranteed by the weak…
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 b…
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…
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…