no code implementations • 1 Jan 2018 • Alexander Zimin, Christoph Lampert
We study conditional risk minimization (CRM), i. e. the problem of learning a hypothesis of minimal risk for prediction at the next step of sequentially arriving dependent data.
no code implementations • 9 Oct 2015 • Alexander Zimin, Christoph H. Lampert
In particular, we aim at learning predictors that minimize the conditional risk for a stochastic process, i. e. the expected loss of the predictor on the next point conditioned on the set of training samples observed so far.
no code implementations • 5 May 2014 • Alexander Zimin, Rasmus Ibsen-Jensen, Krishnendu Chatterjee
We consider the problem of minimizing the regret in stochastic multi-armed bandit, when the measure of goodness of an arm is not the mean return, but some general function of the mean and the variance. We characterize the conditions under which learning is possible and present examples for which no natural algorithm can achieve sublinear regret.
no code implementations • NeurIPS 2013 • Alexander Zimin, Gergely Neu
We study the problem of online learning in finite episodic Markov decision processes where the loss function is allowed to change between episodes.