Collecting labeled data for machine learning models is often expensive and time-consuming. Active learning addresses this challenge by selectively labeling the most informative observations, but when initial labeled data is limited, it becomes difficult to distinguish genuinely informative points from those appearing uncertain primarily due to noise. Ensemble methods like random forests are a powerful approach to quantifying this uncertainty but do so by aggregating all models indiscriminately. This includes poor performing models and redundant models, a problem that worsens in the presence of noisy data. We introduce UNique Rashomon Ensembled Active Learning (UNREAL), which selectively ensembles only distinct models from the Rashomon set, which is the set of nearly optimal models. Restricting ensemble membership to high-performing models with different explanations helps distinguish genuine uncertainty from noise-induced variation. We show that UNREAL achieves faster theoretical convergence rates than traditional active learning approaches and demonstrates empirical improvements of up to 20% in predictive accuracy across five benchmark datasets, while simultaneously enhancing model interpretability.

Y adav and BodeMETHODOLOGY A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training Sumedh Y adav 1* and Mathis Bode 2 * Correspondence: sumedhyadav.iitkgp@gmail.com 1 Gstech T echnology Pvt. Ltd., 415, 2nd Floor, 16th Cross Road, 17th Main Road, HSR Layout Sector 4, 560102, Bengaluru, India Full list of author information is available at the end of the article Abstract A scalable graphical method is presented for selecting, and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is proceeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method constitutes of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is significant reduction in training computation run-time without compromising prediction accuracy . T est results show that both approaches significantly speedup the training task when compared against that of state-of-the-art shrinking heuristic available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy . A network design is also presented for the partitioning based distributed training formulation. Added speedup in training run-time is observed when compared to that of serial implementation of the approaches. Keywords: training set selection; machine learning; large datasets; distributed machine learning; classification; graph coarsening objective; network architecture design Introduction Two decades earlier, some of the most seminal works in machine learning were done on training set selection [1, 2] under the banner of relevance reasoning. However, the better part of recent works have been exclusively towards feature selection [3, 4]. With increased processing power, run time of training is feasible even for datasets erstwhile considered large. Additionally, dimensionality ( d) dominates dataset size ( n) in the algorithmic complexities of learning algorithms. In the training phase, less data points mean fewer generalization guarantees, however, as we are moving in the era of big data, even the fastest classification algorithms are taking unfeasible time to train models. When data sources are abundant, it is befitting to separate data based on relevance to the learning task. This has led to a renewed interest in the once famous problem statement of relevance reasoning [5, 6]. Reasoning on relevance to get improved scalability of classification algorithms is currently explored on graphical/network data [7], and learned models [8]. One research area where training set selection has been given attention to is support vector machines (SVM).