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.

Input: Consider Input: In this paper, we propose the distributed tree kernels (DTK) as a novel method to reduce time and space complexity of tree kernels. Using a linear complexity algorithm to compute vectors for trees, we embed feature spaces of tree fragments in low-dimensional spaces where the kernel computation is directly done with dot product. We show that DTKs are faster, correlate with tree kernels, and obtain a statistically similar performance in two natural language processing tasks.

Output: Distributed Tree Kernels

Input: Consider Input: We study the problem of identifying the best action among a set of possible options when the value of each action is given by a mapping from a number of noisy micro-observables in the so-called fixed confidence setting. Our main motivation is the application to the minimax game search, which has been a major topic of interest in artificial intelligence. In this paper we introduce an abstract setting to clearly describe the essential properties of the problem. While previous work only considered a two-move game tree search problem, our abstract setting can be applied to the general minimax games where the depth can be non-uniform and arbitrary, and transpositions are allowed. We introduce a new algorithm (LUCB-micro) for the abstract setting, and give its lower and upper sample complexity results. Our bounds recover some previous results, which were only available in more limited settings, while they also shed further light on how the structure of minimax problems influence sample complexity.

Output: Structured Best Arm Identification with Fixed Confidence

Input: Consider Input: Abstract The nearest neighbor rule is one of the most widely used models for classification, and selecting a compact set of prototype instances is a primary challenges for its applications. Many existing approaches for prototype selection exploit instance-based analyses and locally-defined criteria on the class distribution, which are intractable for numerical optimization techniques. In this paper, we explore a parametric framework with an adjusted nearest neighbor rule, in which the selection of the neighboring prototypes is modified by their respective parameters. The framework allows us to formulate a minimization problem of the violation of the adjusted nearest neighbor rule over the training set with regards to numerical parameters. We show that the problem reduces to a large-margin principled learning and demonstrate its advantage by empirical comparisons with recent state-ofthe-art methods using public benchmark data.
Output: Discriminative Learning of the Prototype Set for Nearest Neighbor Classification