论文标题
游戏理论算法的有条件矩匹配
Game-Theoretic Algorithms for Conditional Moment Matching
论文作者
论文摘要
计量经济学和机器学习中的各种问题，包括仪器变量回归和钟声残留最小化，可以表达为满足一组条件矩限制（CMR）。我们得出了一种满足CMR的一般游戏理论策略，该策略可扩展到非线性问题，可与基于梯度的优化相提并论，并且能够考虑有限的样本不确定性。我们恢复了Dikkala等人的方法。和Dai等。作为我们一般框架的特殊情况，请先详细介绍各种扩展，以及如何有效地解决CMR定义的游戏。
A variety of problems in econometrics and machine learning, including instrumental variable regression and Bellman residual minimization, can be formulated as satisfying a set of conditional moment restrictions (CMR). We derive a general, game-theoretic strategy for satisfying CMR that scales to nonlinear problems, is amenable to gradient-based optimization, and is able to account for finite sample uncertainty. We recover the approaches of Dikkala et al. and Dai et al. as special cases of our general framework before detailing various extensions and how to efficiently solve the game defined by CMR.
