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This paper introduces Strategic 16K, a leakage-controlled dataset for document sensitivity classification, and benchmarks classical and transformer-based models, with BERT achieving top performance while addressing label leakage issues.
This paper presents a distributed benchmark study on scaling laws for classical machine learning models on tabular data, showing that power-law fits hold for most model families and quantifying replicator-implementation variance across 127 student runs.
Shuo Yang and team release FlashLib, a GPU library that accelerates classical ML operators like KMeans, KNN, HDBSCAN, PCA, and t-SNE, claiming speedups up to 208x.
The Flash-KMeans team releases FlashLib, a GPU library for classical ML operators that achieves up to 208x speedups over cuML on Hopper GPUs, with a focus on fast, predictable performance for agentic AI workloads.