mlsanityData to 7 Oct 2026

Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments

arXiv 2204.08281 ↗First posted 8 Apr 2022math.OC cs.LG stat.MLcited by 31

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Abstract (arXiv)

This paper studies the problem of expected loss minimization given a data distribution that is dependent on the decision-maker's action and evolves dynamically in time according to a geometric decay process. Novel algorithms for both the information setting in which the decision-maker has a first order gradient oracle and the setting in which they have simply a loss function oracle are introduced. The algorithms operate on the same underlying principle: the decision-maker repeatedly deploys a fixed decision over the length of an epoch, thereby allowing the dynamically changing environment to sufficiently mix before updating the decision. The iteration complexity in each of the settings is shown to match existing rates for first and zero order stochastic gradient methods up to logarithmic factors. The algorithms are evaluated on a "semi-synthetic" example using real world data from the SFpark dynamic pricing pilot study; it is shown that the announced prices result in an improvement for the institution's objective (target occupancy), while achieving an overall reduction in parking rates.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 8 Apr 2022.

Builds on

10 of 37 references.

References · 10

  1. 2020Performative Predictioncited by 318
  2. 2019A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Normcited by 105
  3. 2021Outside the Echo Chamber: Optimizing the Performative Riskcited by 95
  4. 2020Stochastic Optimization for Performative Predictioncited by 92
  5. 2020Performative Prediction in a Stateful Worldcited by 90
  6. 2016The Landscape of Empirical Risk for Non-convex Lossescited by 86
  7. 2020Stochastic optimization with decision-dependent distributionscited by 65
  8. 2018Bandit learning in concave N-person gamescited by 32
  9. 2020Algorithms for Hiring and Outsourcing in the Online Labor Marketcited by 5
  10. 2017Optimizing Curbside Parking Resources Subject to Congestion Constraintscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 7; 2024: 6; 2025: 8; 2026: 6.

31 papers.

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  1. 2023Performative Prediction: Past and Futurecited by 52
  2. 2023Performative Prediction with Neural Networkscited by 33
  3. 2022Preference Dynamics Under Personalized Recommendationscited by 25
  4. 2023Plug-in Performative Optimizationcited by 20
  5. 2023Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shiftscited by 14
  6. 2024Performative Reinforcement Learning in Gradually Shifting Environmentscited by 13
  7. 2022Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimalitycited by 12
  8. 2023Performative Prediction with Bandit Feedback: Learning through Reparameterizationcited by 11
  9. 2023Zero-Regret Performative Prediction Under Inequality Constraintscited by 10
  10. 2024Performative Reinforcement Learning with Linear Markov Decision Processcited by 8
  11. 2024Distributionally Robust Performative Predictioncited by 7
  12. 2022Online Convex Optimization with Unbounded Memorycited by 6
19 more
  1. 2022Emergent specialization from participation dynamics and multi-learner retrainingcited by 5
  2. 2023Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flowscited by 5
  3. 2025Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamicscited by 5
  4. 2025Independent Learning in Performative Markov Potential Gamescited by 5
  5. 2025Performative Policy Gradient: Optimality in Performative Reinforcement Learningcited by 4
  6. 2026The Stability of Online Algorithms in Performative Predictioncited by 4
  7. 2025On the Impact of Performative Risk Minimization for Binary Random Variablescited by 3
  8. 2024Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributionscited by 2
  9. 2024Risk-averse Learning with Non-Stationary Distributionscited by 2
  10. 2025Achieve Performatively Optimal Policy for Performative Reinforcement Learningcited by 2
  11. 2026Dissecting Performative Prediction: A Comprehensive Surveycited by 2
  12. 2026Partially Performative Predictioncited by 2
  13. 2025SPRINT: Stochastic Performative Prediction With Variance Reductioncited by 1
  14. 2026Retraining Seeks Stable Signalscited by 1
  15. 2024Risk-averse learning with delayed feedbackcited by 0
  16. 2025Evolutionary Prediction Gamescited by 0
  17. 2025Risk-Averse Learning with Varying Risk Levelscited by 0
  18. 2026When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learningcited by 0
  19. 2026Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Predictioncited by 0