Dec 6, 2021
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The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that can interact with humans. Many building blocks are essential to this activity, and one of the central ones is the algorithmic characterization of aspects of human behavior. While much of the existing work focuses on characterizing human behavior in an aggregate or generic sense, an important longer-range goal is to specialize these characterizations to individual people. Specializing behavioral models to individual people requires that we be able to characterize what differentiates specific people. As a means of formalizing this process, we study the problem of behavioral stylometry, in which the task is to identify a decision-maker from their decisions alone. We present a transformer-based approach to behavioral stylometry in the context of chess, in which a game's players are identified from their moves. Our method operates in a few-shot classification framework, and can correctly identify a player from among thousands of candidate players given only 100 labeled games with up to 98The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that can interact with humans. Many building blocks are essential to this activity, and one of the central ones is the algorithmic characterization of aspects of human behavior. While much of the existing work focuses on characterizing human behavior in an aggregate or generic sense, an important longer-range goal is to specialize these characte…
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Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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