# Performative Prediction with Bandit Feedback: Learning through Reparameterization arXiv: https://arxiv.org/abs/2305.01094 ยท HTML: https://mlsanity.com/paper/2305.01094/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 36 references that are in the map. - First posted on arXiv 1 May 2023. ## Builds on (most cited first, 28 of 28) - [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020) - [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015) - [A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning](https://mlsanity.com/paper/2006.06224/) (2020) - [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017) - [An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback](https://mlsanity.com/paper/1507.08752/) (2015) - [Outside the Echo Chamber: Optimizing the Performative Risk](https://mlsanity.com/paper/2102.08570/) (2021) - [Stochastic Optimization for Performative Prediction](https://mlsanity.com/paper/2006.06887/) (2020) - [Performative Prediction in a Stateful World](https://mlsanity.com/paper/2011.03885/) (2020) - [How Do Classifiers Induce Agents To Invest Effort Strategically?](https://mlsanity.com/paper/1807.05307/) (2018) - [How to Learn when Data Reacts to Your Model: Performative Gradient Descent](https://mlsanity.com/paper/2102.07698/) (2021) - [Stochastic optimization with decision-dependent distributions](https://mlsanity.com/paper/2011.11173/) (2020) - [Multiplayer Performative Prediction: Learning in Decision-Dependent Games](https://mlsanity.com/paper/2201.03398/) (2022) - [Performative Power](https://mlsanity.com/paper/2203.17232/) (2022) - [Regret Minimization with Performative Feedback](https://mlsanity.com/paper/2202.00628/) (2022) - [State Dependent Performative Prediction with Stochastic Approximation](https://mlsanity.com/paper/2110.00800/) (2021) - [Anticipating Performativity by Predicting from Predictions](https://mlsanity.com/paper/2208.07331/) (2022) - [Performative Prediction with Neural Networks](https://mlsanity.com/paper/2304.06879/) (2023) - [Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos](https://mlsanity.com/paper/2201.10483/) (2022) - [Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments](https://mlsanity.com/paper/2204.08281/) (2022) - [Practical and Consistent Estimation of f-Divergences](https://mlsanity.com/paper/1905.11112/) (2019) - [Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents](https://mlsanity.com/paper/2209.03811/) (2022) - [Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization](https://mlsanity.com/paper/2106.09082/) (2021) - [Watch and Learn: Optimizing from Revealed Preferences Feedback](https://mlsanity.com/paper/1504.01033/) (2015) - [Linear Classifiers that Encourage Constructive Adaptation](https://mlsanity.com/paper/2011.00355/) (2020) - [Stochastic Optimization under Distributional Drift](https://mlsanity.com/paper/2108.07356/) (2021) - [Approximate Regions of Attraction in Learning with Decision-Dependent Distributions](https://mlsanity.com/paper/2107.00055/) (2021) - [Model Transferability With Responsive Decision Subjects](https://mlsanity.com/paper/2107.05911/) (2021) - [Online Saddle Point Tracking with Decision-Dependent Data](https://mlsanity.com/paper/2212.02693/) (2022) ## Built on by (most cited first, 11 of 11) - [Strategic Apple Tasting](https://mlsanity.com/paper/2306.06250/) (2023) - [Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions](https://mlsanity.com/paper/2412.20330/) (2024) - [Contextual Dynamic Pricing with Strategic Buyers](https://mlsanity.com/paper/2307.04055/) (2023) - [Goal Recognition Design for General Behavioral Agents using Machine Learning](https://mlsanity.com/paper/2404.03054/) (2024) - [ProFL: Performative Robust Optimal Federated Learning](https://mlsanity.com/paper/2410.18075/) (2024) - [Nonlinear Performative Prediction](https://mlsanity.com/paper/2509.01139/) (2025) - [Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop](https://mlsanity.com/paper/2601.05184/) (2026) - [Dissecting Performative Prediction: A Comprehensive Survey](https://mlsanity.com/paper/2602.10176/) (2026) - [Partially Performative Prediction](https://mlsanity.com/paper/2606.07890/) (2026) - [Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity](https://mlsanity.com/paper/2505.24097/) (2025) - [When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning](https://mlsanity.com/paper/2601.04447/) (2026) ## Abstract (arXiv) Performative prediction, as introduced by Perdomo et al, is a framework for studying social prediction in which the data distribution itself changes in response to the deployment of a model. Existing work in this field usually hinges on three assumptions that are easily violated in practice: that the performative risk is convex over the deployed model, that the mapping from the model to the data distribution is known to the model designer in advance, and the first-order information of the performative risk is available. In this paper, we initiate the study of performative prediction problems that do not require these assumptions. Specifically, we develop a reparameterization framework that reparametrizes the performative prediction objective as a function of the induced data distribution. We then develop a two-level zeroth-order optimization procedure, where the first level performs iterative optimization on the distribution parameter space, and the second level learns the model that induces a particular target distribution at each iteration. Under mild conditions, this reparameterization allows us to transform the non-convex objective into a convex one and achieve provable regret guarantees. In particular, we provide a regret bound that is sublinear in the total number of performative samples taken and is only polynomial in the dimension of the model parameter.