Predictor-corrector algorithms for stochastic optimization under gradual distribution shift

• URL: http://arxiv.org/abs/2205.13575v1
• Date: Thu, 26 May 2022 18:33:00 GMT
• Title: Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
• Authors: Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun
• Abstract summary: Time-varying optimization problems frequently arise in machine learning practice. We exploit this underlying continuity by developing predictor-corrector algorithms for time-varying optimizations.
• Score: 26.897316325189212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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