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.

Example Input: The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly wellsuited for modeling settings that elicit subjective or implicit human feedback. In this paper, we study the problem of multi-dueling bandits with dependent arms, which extends the original dueling bandits setting by simultaneously dueling multiple arms as well as modeling dependencies between arms. These extensions capture key characteristics found in many realworld applications, and allow for the opportunity to develop significantly more efficient algorithms than were possible in the original setting. We propose the SELFSPARRING algorithm, which reduces the multi-dueling bandits problem to a conventional bandit setting that can be solved using a stochastic bandit algorithm such as Thompson Sampling, and can naturally model dependencies using a Gaussian process prior. We present a no-regret analysis for multi-dueling setting, and demonstrate the effectiveness of our algorithm empirically on a wide range of simulation settings.
Example Output: Multi-dueling Bandits with Dependent Arms

Example Input: There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn­ ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern­ ing how the presence of one cause of an effect makes an­ other cause less probable) support the hypothesis that humans reach the same judgments as algorithms for do­ ing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and de­ pendencies as those used by certain algorithms for learn­ ing the structure of a Bayesian network. Based on this indication, a subjective definition of causality is for­ warded. Finally, methods for further testing the accu­ racy of these claims are discussed.
Example Output: THE CoGNITIVE PROCESSING OF CAUSAL KNOWLEDGE

Example Input: To coordinate with other agents in its envi­ ronment, an agent needs models of what the other agents are trying to do. When com­ munication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be follow­ ing, and develop special techniques for dis­ criminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures sup­ porting inference based on uncertain and in­ complete observations. In this paper, we de­ scribe a set of methods for converting plans represented in a flexible procedural language to observation models represented as proba­ bilistic belief networks.
Example Output:
The Automated Mapping of Plans for Plan Recognition*