# Learning from Streaming Data when Users Choose

arXiv: https://arxiv.org/abs/2406.01481 · HTML: https://mlsanity.com/paper/2406.01481/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 30 of 30)

- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Momentum-Based Variance Reduction in Non-Convex SGD](https://mlsanity.com/paper/1905.10018/) (2019)
- [Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition](https://mlsanity.com/paper/1503.02101/) (2015)
- [On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes](https://mlsanity.com/paper/1805.08114/) (2018)
- [Online stochastic gradient descent on non-convex losses from high-dimensional inference](https://mlsanity.com/paper/2003.10409/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Multiplayer Performative Prediction: Learning in Decision-Dependent Games](https://mlsanity.com/paper/2201.03398/) (2022)
- [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://mlsanity.com/paper/2108.09355/) (2021)
- [Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness](https://mlsanity.com/paper/1905.00569/) (2019)
- [Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos](https://mlsanity.com/paper/2201.10483/) (2022)
- [Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents](https://mlsanity.com/paper/2209.03811/) (2022)
- [Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning](https://mlsanity.com/paper/2111.12050/) (2021)
- [Online Projected Gradient Descent for Stochastic Optimization with Decision-Dependent Distributions](https://mlsanity.com/paper/2107.09721/) (2021)
- [Competition, Alignment, and Equilibria in Digital Marketplaces](https://mlsanity.com/paper/2208.14423/) (2022)
- [Regression Equilibrium](https://mlsanity.com/paper/1905.02576/) (2019)
- [Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition](https://mlsanity.com/paper/2306.14670/) (2023)
- [Online k-means Clustering](https://mlsanity.com/paper/1909.06861/) (2019)
- [Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima](https://mlsanity.com/paper/2003.02818/) (2020)
- [An Algorithm for Online K-Means Clustering](https://mlsanity.com/paper/1412.5721/) (2014)
- [Convergence rate of stochastic k-means](https://mlsanity.com/paper/1611.05132/) (2016)
- [Stochastic Nonconvex Optimization with Large Minibatches](https://mlsanity.com/paper/1709.08728/) (2017)
- [Distributed Stochastic Optimization via Adaptive SGD](https://mlsanity.com/paper/1802.05811/) (2018)
- [Competition over data: how does data purchase affect users?](https://mlsanity.com/paper/2201.10774/) (2022)
- [Emergent specialization from participation dynamics and multi-learner retraining](https://mlsanity.com/paper/2206.02667/) (2022)
- [Asymptotic Analysis of Conditioned Stochastic Gradient Descent](https://mlsanity.com/paper/2006.02745/) (2020)
- [Convergence of online k-means](https://mlsanity.com/paper/2202.10640/) (2022)
- [Strategic Usage in a Multi-Learner Setting](https://mlsanity.com/paper/2401.16422/) (2024)
- [Leveraging User Behavior History for Personalized Email Search](https://mlsanity.com/paper/2102.07279/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Market for Accuracy: Classification under Competition](https://mlsanity.com/paper/2502.18052/) (2025)
- [Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry](https://mlsanity.com/paper/2409.03734/) (2024)

## Abstract (arXiv)

In digital markets comprised of many competing services, each user chooses between multiple service providers according to their preferences, and the chosen service makes use of the user data to incrementally improve its model. The service providers' models influence which service the user will choose at the next time step, and the user's choice, in return, influences the model update, leading to a feedback loop. In this paper, we formalize the above dynamics and develop a simple and efficient decentralized algorithm to locally minimize the overall user loss. Theoretically, we show that our algorithm asymptotically converges to stationary points of of the overall loss almost surely. We also experimentally demonstrate the utility of our algorithm with real world data.