@inproceedings{
silva2024prospective,
title={Prospective Learning: Learning for a Dynamic Future},
author={Ashwin De Silva and Rahul Ramesh and Rubing Yang and Siyu Yu and Joshua T Vogelstein and Pratik Chaudhari},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=XEbPJUQzs3}
}
In real-world applications, the distribution of the data, and our goals, evolve over time. The prevailing theoretical framework for studying machine learning, namely probably approximately correct (PAC) learning, largely ignores time. As a consequence, existing strategies to address the dynamic nature of data and goals exhibit poor real-world performance. This paper develops a theoretical framework called
"Prospective Learning" that is tailored for situations when the optimal hypothesis changes over time. In PAC learning, empirical risk minimization (ERM) is known to be consistent. We develop a learner called Prospective ERM, which returns a sequence of predictors that  make predictions on future data.  We prove that the risk of prospective ERM converges to the Bayes risk under certain assumptions on the stochastic process  generating the data. Prospective ERM, roughly speaking, incorporates time as an input in addition to the data. We show that standard ERM as done in PAC learning, without incorporating time, can result in failure to learn when distributions are dynamic. Numerical experiments illustrate that prospective ERM can learn synthetic and visual recognition problems constructed from MNIST and CIFAR-10.
Prospective Learning: Learning for a Dynamic Future
Ashwin De Silva Rahul Ramesh Rubing Yang Siyu Yu Joshua T Vogelstein Pratik Chaudhari