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stat.ML2026
Scaling Laws for Precision in High-Dimensional Linear Regression
Dechen Zhang, Xuan Tang, Yingyu Liang +1
Low-precision training is critical for optimizing the trade-off between model quality and training costs, necessitating the joint allocation of model size, dataset size, and numeri…
stat.ML2025
Kernel Regression in Structured Non-IID Settings: Theory and Implications for Denoising Score Learning
Dechen Zhang, Zhenmei Shi, Yi Zhang +2
Kernel ridge regression (KRR) is a foundational tool in machine learning, with recent work emphasizing its connections to neural networks. However, existing theory primarily addres…
stat.ML2025
Learning under Quantization for High-Dimensional Linear Regression
Dechen Zhang, Junwei Su, Difan Zou
The use of low-bit quantization has emerged as an indispensable technique for enabling the efficient training of large-scale models. Despite its widespread empirical success, a rig…