  The goal of coreset selection in supervised learning is to produce a weighted
subset of data, so that training only on the subset achieves similar
performance as training on the entire dataset. Existing methods achieved
promising results in resource-constrained scenarios such as continual learning
and streaming. However, most of the existing algorithms are limited to
traditional machine learning models. A few algorithms that can handle large
models adopt greedy search approaches due to the difficulty in solving the
discrete subset selection problem, which is computationally costly when coreset
becomes larger and often produces suboptimal results. In this work, for the
first time we propose a continuous probabilistic bilevel formulation of coreset
selection by learning a probablistic weight for each training sample. The
overall objective is posed as a bilevel optimization problem, where 1) the
inner loop samples coresets and train the model to convergence and 2) the outer
loop updates the sample probability progressively according to the model's
performance. Importantly, we develop an efficient solver to the bilevel
optimization problem via unbiased policy gradient without trouble of implicit
differentiation. We provide the convergence property of our training procedure
and demonstrate the superiority of our algorithm against various coreset
selection methods in various tasks, especially in more challenging label-noise
and class-imbalance scenarios.
