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.

本文针对机器学习中涉及到的时间变化的随机优化问题，以及其在离散时间的解法中存在的连续性问题，提出了基于预测 - 校正算法的优化方法，并给出了理论与实验证明，相较于不使用该算法的其他方法，本文提出的算法能更好地利用连续过程，提高最优解的准确性和鲁棒性。