As an essential component of fine-tuning, warm-up plays a crucial role in promoting stability and generalization. Many studies have examined its underlying mechanisms from different aspects. However, most of the studies focus on incorporating these insights into optimizers to reduce the reliance on warm-up. Little attention has been paid to addressing the inherent limitations of the warm-up itself, which restricts its effectiveness. In this work, we revisit warm-up from a loss landscape perspective and identify several limitations with existing warm-up, including: (1) susceptibility to nearby suboptimal traps, (2) sensitivity to hyperparameters and random seeds, and (3) inefficiency during the early stages of training. To overcome these limitations, we propose Sensitivity-Aware Warm-Up (SAWU), a lightweight and adaptive strategy that dynamically leverages learning sensitivity during warm-up to guide updates toward better and more stable basins. In addition, SAWU also introduces an adaptive scheduling mechanism and phase transition strategy across warm-up, stable, and decay phases to further enhance robustness and efficiency. Extensive experiments on various downstream tasks show that SAWU significantly outperforms the vanilla method (e.g., average 3.43% improvement on RoBerta). Moreover, SAWU can be easily combined with various optimizers and remains effective even when warm-up-based methods fail (e.g, it lifts RAdam from 49.46% to 91.78% on qnli. Thanks to its lightweight nature, SAWU introduces minimal overhead and even reduces training time by over 5% compared to other methods.

Feature engineering plays a critical role in machine learning (ML), but existing methods often struggle with high computational cost and limited scalability when applied to large-scale and sparse datasets. In this article, we propose EaSFE, an efficient and scalable feature engineering framework that unifies feature generation, filtering, and evaluation in an end-to-end manner. EaSFE is designed to efficiently construct and select informative features while explicitly considering computational and memory constraints. To achieve scalability, EaSFE incorporates parallel and distributed execution mechanisms, as well as a chunk-based data processing strategy that enables memory-efficient feature engineering on large datasets. In addition, EaSFE adopts tailored storage and execution strategies to handle high-dimensional sparse data effectively. Extensive experiments on multiple real-world datasets demonstrate that EaSFE consistently improves predictive performance (e.g., 5% accuracy improvement in poker ) while substantially enhancing efficiency (i.e., over 10x speedup) compared to existing feature engineering methods. In addition, EaSFE is demonstrated to scale to large and sparse datasets, successfully handling datasets with over 119 million training instances and 54 million features.

Data science aims to solve real-world problems with the knowledge derived from data. Successfully tackling a data science problem requires practitioners to choose an appropriate solution, which potentially comprises various components such as pre-processing techniques, learning algorithms, hyper-parameters, and so on. Therefore, a problem-driven recommendation for the promising solution is invaluable, as it facilitates efficient and convenient problem-solving. However, existing solution recommendation approaches confront notable challenges when dealing with limited and sparse prior experience in practical applications. Learning from such prior easily leads to overfitting and poor generalization in solution recommendations. To address this issue, we propose a novel solution recommendation method that can predict a good-quality data science solution, including the pre-processing, the learning algorithm, and hyper-parameters, for a given problem. The foundation of our method is a carefully designed ranking model that exploits a weight-sharing structure and a newly proposed loss. The ranking model focuses on incorporating relative ranking information into the predicted performance score of each solution. With these techniques, our method can recommend the solution with the highest score and effectively mitigate the limitations of using sparse prior experience. Our experiments demonstrate the superiority of our method in predicting solutions with higher accuracy and rank, even trained on highly sparse historical performance records. It also reduces recommendation time significantly compared to the baselines, offering remarkable efficiency and convenience for practitioners.

Bandit-based methods are commonly used for hyperparameter optimization (HPO), which is significant in data analytics. When confronted with numerous configurations and high-dimensional large problems, existing bandit-based methods face challenges of high evaluation cost and poor optimization performance. To address these challenges, we introduce an improved bandit-based approach that exhibits enhanced evaluation ability and is suitable for situations with limited resources. Specifically, our method first effectively utilizes the feature and label information to conduct representative groups for further evaluation. After that, two kinds of folds (i.e., general folds and special folds) are constructed to facilitate better evaluation of the configuration in the cross-validation process. Additionally, we incorporate variance and subset size into the evaluation metric to comprehensively evaluate the configuration. We integrate our proposed method into three commonly used bandit-based methods, and experimental results on multiple datasets show that our method has advantages in stability with accuracy improvement of 1 % to 15 % on the datasets tested. In addition, since our method can avoid configurations that are low-quality but time-consuming to evaluate, it is always more efficient than the existing bandit-based methods, and can even reduce the execution time by half in some datasets. Sometimes it takes a little more time, but the improvement in accuracy can be significant.

Second-order optimization has been recently explored in neural network training. However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training. There have been some attempts to address this issue by approximation on the Hessian matrix, which unfortunately degrades the performance of the neural models. In order to tackle this issue, we propose Kernel Stochastic Gradient Descent (Kernel SGD) which solves the optimization problem in a space transformed by the Hessian matrix of the kernel machine. Kernel SGD eliminates the Hessian matrix recomputation in the training and requires a much smaller memory cost which can be controlled via the mini-batch size. We show that Kernel SGD optimization is theoretically guaranteed to converge. Our experimental results on tabular, image and text data confirm that Kernel SGD converges up to 30 times faster than the existing second-order optimization techniques, and achieves the highest test accuracy on all the tasks tested. Kernel SGD even outperforms the first-order optimization baselines in some problems tested in our experiments.