Review for NeurIPS paper: Stochastic Optimization for Performative Prediction

Additional Feedback: This paper presents that different time intervals at which they deploy models trained with stochastic gradient descent in performative prediction leads to qualitatively different algorithms. Moreover, a series of experimental results confirm the result of theoretical analysis of greedy and lazy deploy. Although the paper is theoretically sound, there are still some questions need to be discussed in this paper: 1. This paper uses stochastic gradient methods to solve performative prediction, which is similar to the previous work [R1]. In addition, the proof in this paper seems an extension of the prior work [R1] on performative prediction. The authors should give the key contribution of this paper and discuss the difference between them.