Online-learning research has mainly been focusing on minimizing one objective function. In many real-world applications, however, several objective functions have to be considered simultaneously.

In many real-world applications, however, several objective functions have to be considered simultaneously.

This paper presents an asymptotic analysis for nonparametric sequential prediction when there are multiple objectives. While the paper has some relevance to a certain subset of the machine learning community, I have some concerns about the paper's relevance to NIPS, and I also am unclear on some of the basic setup of the paper. I discuss these issues in the remainder of the review. Overall, I think the results in the paper are technically strong and, with the right motivation for \gamma -feasibility (see below) I would be weakly positive on the paper. Relevance: While I can see that the contents of the paper have some relevance to machine learning, I feel that this sort of paper would fit much better in a conference like COLT or ALT, given the technical level and given how much this paper can benefit from the additional space offered by those venues.

Neural Information Processing Systems http://nips.cc/

Online-learning research has mainly been focusing on minimizing one objective function. In many real-world applications, however, several objective functions have to be considered simultaneously. In this paper, we extend the multi-objective framework to the case of stationary and ergodic processes, thus allowing dependencies among observations. We first identify an asymptomatic lower bound for any prediction strategy and then present an algorithm whose predictions achieve the optimal solution while fulfilling any continuous and convex constraining criterion. Papers published at the Neural Information Processing Systems Conference.

Online-learning research has mainly been focusing on minimizing one objective function. In many real-world applications, however, several objective functions have to be considered simultaneously. Recently, an algorithm for dealing with several objective functions in the i.i.d. case has been presented. In this paper, we extend the multi-objective framework to the case of stationary and ergodic processes, thus allowing dependencies among observations. We first identify an asymptomatic lower bound for any prediction strategy and then present an algorithm whose predictions achieve the optimal solution while fulfilling any continuous and convex constraining criterion.