38 citations · 133 across the 25 of their papers we have counts for
4 papers · 1 filter
On PAC-Bayesian Bounds for Random Forests
Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods…
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Julian Zimmert, Yevgeny Seldin
We derive an algorithm that achieves the optimal (within constants) pseudo-regret in both adversarial and stochastic multi-armed bandits without prior knowledge of the regime and t…
Factored Bandits
Julian Zimmert, Yevgeny Seldin
We introduce the factored bandits model, which is a framework for learning with limited (bandit) feedback, where actions can be decomposed into a Cartesian product of atomic action…
Adaptation to Easy Data in Prediction with Limited Advice
Tobias Sommer Thune, Yevgeny Seldin
We derive an online learning algorithm with improved regret guarantees for `easy' loss sequences. We consider two types of `easiness': (a) stochastic loss sequences and (b) adversa…