Supervised machine learning often encounters concept drift, where the data distribution changes over time, degrading model performance. Existing drift detection methods focus on identifying these shifts but often overlook the challenge of acquiring labeled data for model retraining after a shift occurs. We present the Strategy for Drift Sampling (SUDS), a novel method that selects homogeneous samples for retraining using existing drift detection algorithms, thereby enhancing model adaptability to evolving data. SUDS seamlessly integrates with current drift detection techniques. We also introduce the Harmonized Annotated Data Accuracy Metric (HADAM), a metric that evaluates classifier performance in relation to the quantity of annotated data required to achieve the stated performance, thereby taking into account the difficulty of acquiring labeled data. Our contributions are twofold: SUDS combines drift detection with strategic sampling to improve the retraining process, and HADAM provides a metric that balances classifier performance with the amount of labeled data, ensuring efficient resource utilization. Empirical results demonstrate the efficacy of SUDS in optimizing labeled data use in dynamic environments, significantly improving the performance of machine learning applications in real-world scenarios. Our code is open source and available at https://github.com/cfellicious/SUDS/

This paper presents a framework for smooth optimization of objectives with $\ell_q$ and $\ell_{p,q}$ regularization for (structured) sparsity. Finding solutions to these non-smooth and possibly non-convex problems typically relies on specialized optimization routines. In contrast, the method studied here is compatible with off-the-shelf (stochastic) gradient descent that is ubiquitous in deep learning, thereby enabling differentiable sparse regularization without approximations. The proposed optimization transfer comprises an overparametrization of selected model parameters followed by a change of penalties. In the overparametrized problem, smooth and convex $\ell_2$ regularization induces non-smooth and non-convex regularization in the original parametrization. We show that the resulting surrogate problem not only has an identical global optimum but also exactly preserves the local minima. This is particularly useful in non-convex regularization, where finding global solutions is NP-hard and local minima often generalize well. We provide an integrative overview that consolidates various literature strands on sparsity-inducing parametrizations in a general setting and meaningfully extend existing approaches. The feasibility of our approach is evaluated through numerical experiments, demonstrating its effectiveness by matching or outperforming common implementations of convex and non-convex regularizers.

In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments. While Adam is an adaptive lower-order moment based (of the stochastic gradient) method, we propose an extension namely, HAdam, which uses higher order moments of the stochastic gradient. Our analysis and experiments reveal that certain higher-order moments of the stochastic gradient are able to achieve better performance compared to the vanilla Adam algorithm. We also provide some analysis of HAdam related to odd and even moments to explain some intriguing and seemingly non-intuitive empirical results.