In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: We consider a general framework of online learning with expert advice where the regret is defined with respect to a competitor class defined by a weighted automaton over sequences of experts. Our framework covers several problems previously studied, in particular that of competing against k-shifting experts. We give a series of algorithms for this problem, including an automata-based algorithm extending weightedmajority and more efficient algorithms based on the notion of failure transitions. We further present efficient algorithms based on a compact approximation of the competitor automaton, in particular efficient n-gram models obtained by minimizing the Rényi divergence, and present an extensive study of the approximation properties of such models. We also extend our algorithms and results to the framework of sleeping experts. Finally, we describe the extension of our approximation methods to online convex optimization and a general mirror descent setting.

Answer: Online Learning against Expert Automata


Question: We provide a general framework for computing lower-bounds on the sample complexity of recovering the underlying graphs of Ising models, given i.i.d. samples. While there have been recent results for specific graph classes, these involve fairly extensive technical arguments that are specialized to each specific graph class. In contrast, we isolate two key graph-structural ingredients that can then be used to specify sample complexity lower-bounds. Presence of these structural properties makes the graph class hard to learn. We derive corollaries of our main result that not only recover existing recent results, but also provide lower bounds for novel graph classes not considered previously. We also extend our framework to the random graph setting and derive corollaries for Erdős-Rényi graphs in a certain dense setting.

Answer: On the Information Theoretic Limits of Learning Ising Models


Question: Influence diagrams are decision theoretic extensions of Bayesian networks. They are applied to diverse decision problems. In this paper we apply influence diagrams to the optimization of a vehicle speed profile. We present results of computational experiments in which an influence diagram was used to optimize the speed profile of a Formula 1 race car at the Silverstone F1 circuit. The computed lap time and speed profiles correspond well to those achieved by test pilots. An extended version of our model that considers a more complex optimization function and diverse traffic constraints is currently being tested onboard a testing car by a major car manufacturer. This paper opens doors for new applications of influence diagrams.

Answer:
Influence diagrams for the optimization of a vehicle speed profile