发表机构
MIT EECS; New York University（麻省理工学院电气工程与计算机科学系; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种新算法，在高精度下以近乎最少的模型部署次数找到性能稳定预测器，无需假设预测如何影响分布，且部署次数比先前方法指数级减少，并给出去随机化结果。
AI 中文摘要
当算法预测影响人们的决策时，我们部署的模型具有性能性，并主动塑造我们观察到的数据。算法与其更广泛环境之间的这种反馈循环给社会预测机制带来了挑战：如果不同的预测模型引发不同的数据分布，是否有可能高效地学习一个对其所引发分布最优的预测规则？形式上，这一解决方案概念被称为性能稳定性。学习性能稳定预测器的一个核心挑战是，与分布固定的监督学习不同，学习者必须部署不同的预测器并观察它们引发的分布。我们工作的主要贡献是一种新的算法流程，在高精度区域，它能在几乎最少的模型部署次数内找到性能稳定的模型，且不对预测如何塑造分布做任何假设。特别是，我们的流程能以比先前方法指数级更少的模型部署次数成功找到随机性能稳定的预测器。我们的第二个主要贡献是一个结构性结果，展示了如何将我们算法实现的这种近期随机稳定性概念去随机化为满足先前确定性概念的单一预测器，前提是愿意假设损失函数条件良好且性能效应较弱，正如该领域早期工作所假设的那样。在技术层面，我们的结果源于建立性能稳定性与期望变分不等式之间一个未被充分探索的技术联系。
英文摘要
When algorithmic predictions inform people's decisions, the models we deploy are performative and actively shape the data we see. This feedback loop between algorithms and their broader environments introduces a challenge in the mechanics of social prediction: If different predictive models induce different distributions, is it possible to efficiently learn a prediction rule that is optimal for the distribution that it induces? Formally, this solution concept is known as performative stability. A core challenge in learning a performatively stable predictor is that, unlike supervised learning where distributions are fixed, the learner must deploy different predictors and observe their induced distributions. The main contribution of our work is a new algorithmic procedure that, in the high-accuracy regime, finds a performatively stable model in nearly the minimum number of model deployments without making any assumptions regarding how predictions shape distributions. In particular, our procedure succeeds at finding a randomized performatively stable predictor using exponentially fewer model deployments than prior approaches. Our second main contribution is a structural result showing how this recent randomized notion of stability achieved by our algorithm can be derandomized into a single predictor satisfying the prior deterministic notion if one is willing to assume that the loss is well-conditioned and that performative effects are weak, as in early work in this area. On a technical level, our results come from building on an underexplored technical connection between performative stability and expected variational inequalities.
