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cs.LG2026
TabNSM: Neural Sparse Mixer for Tabular Regression
Ali Eslamian, Qiang Cheng
Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while deep models enable flexible fe…
cs.LG2026
Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks
Andrew Cheng, Ali Eslamian, Jie Cheng +2
Neural networks can often be trained or fine-tuned through random low-dimensional reparameterization, where a small latent vector is mapped into a full parameter update by a frozen…