DeepVoting：利用规范嵌入学习与微调投票规则
DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings
AI总结：
本文提出DeepVoting，利用神经网络学习概率社会选择函数，通过标准嵌入高效学习投票规则，并利用公理属性微调以抵抗无显示悖论攻击。
摘要：
将智能体的偏好聚合为集体决策是许多问题（如招聘、选举、同行评审）以及计算机科学多个领域（如强化学习、推荐系统）中的重要步骤。正如社会选择理论所示，设计具有特定属性集（公理）的聚合规则可能很困难，在某些情况下甚至被证明是不可能的。与其手工设计算法，不如从数据中学习聚合规则，尤其是投票规则。然而，该领域的先前工作要么需要极大的模型，要么受限于偏好表示（即嵌入）的选择。我们将设计具有理想属性的投票规则问题重新表述为学习输出候选集上分布的概率函数问题。具体而言，我们使用神经网络来学习概率社会选择函数。利用社会选择文献中的标准嵌入，我们表明偏好档案编码对神经网络学习规则的效率和能力有显著影响，使我们能够用比先前工作更小的网络更快地学习规则。此外，我们表明，所学规则可以通过公理属性进行微调，以创建新颖的投票规则，并使其对特定类型的“攻击”具有抵抗力。即，我们微调规则以抵抗无显示悖论的概率版本。
英文摘要：
Aggregating agent preferences into a collective decision is an important step in many problems (e.g., hiring, elections, peer review) and across areas of computer science (e.g., reinforcement learning, recommender systems). As Social Choice Theory has shown, the problem of designing aggregation rules with specific sets of properties (axioms) can be difficult, or provably impossible in some cases. Instead of designing algorithms by hand, one can learn aggregation rules, particularly voting rules, from data. However, prior work in this area has required extremely large models or been limited by the choice of preference representation, i.e., embedding. We recast the problem of designing voting rules with desirable properties into one of learning probabilistic functions that output distributions over a set of candidates. Specifically, we use neural networks to learn probabilistic social choice functions. Using standard embeddings from the social choice literature we show that preference profile encoding has significant impact on the efficiency and ability of neural networks to learn rules, allowing us to learn rules faster and with smaller networks than previous work. Moreover, we show that our learned rules can be fine-tuned using axiomatic properties to create novel voting rules and make them resistant to specific types of "attack". Namely, we fine-tune rules to resist a probabilistic version of the No Show Paradox.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
