# Predictor-corrector algorithms for stochastic optimization under gradual distribution shift

arXiv: https://arxiv.org/abs/2205.13575 · HTML: https://mlsanity.com/paper/2205.13575/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 10 of 10)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [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)
- [Online Learning with Inexact Proximal Online Gradient Descent Algorithms](https://mlsanity.com/paper/1806.00202/) (2018)
- [Prediction-Correction Interior-Point Method for Time-Varying Convex Optimization](https://mlsanity.com/paper/1608.07544/) (2016)
- [Prediction-Correction Algorithms for Time-Varying Constrained Optimization](https://mlsanity.com/paper/1611.03681/) (2016)
- [Online Gradient Descent for Linear Dynamical Systems](https://mlsanity.com/paper/1912.09311/) (2019)
- [A semi-parametric model for target localization in distributed systems](https://mlsanity.com/paper/2012.02025/) (2020)

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

- [Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets](https://mlsanity.com/paper/2404.13240/) (2024)
- [An adaptive transfer learning perspective on classification in non-stationary environments](https://mlsanity.com/paper/2405.18091/) (2024)

## Abstract (arXiv)

Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most problems are solved in discrete time, the underlying process is often continuous in nature. We exploit this underlying continuity by developing predictor-corrector algorithms for time-varying stochastic optimizations. We provide error bounds for the iterates, both in presence of pure and noisy access to the queries from the relevant derivatives of the loss function. Furthermore, we show (theoretically and empirically in several examples) that our method outperforms non-predictor corrector methods that do not exploit the underlying continuous process.