4 papers
Tactics for Improving Least Squares Estimation
Qiang Heng, Hua Zhou, Kenneth Lange
This paper deals with tactics for fast computation in least squares regression in high dimensions. These tactics include: (a) the majorization-minimization (MM) principle, (b) smoo…
An Interpretable and Scalable Framework for Evaluating Large Language Models
Xinhao Qu, Qiang Heng, Hao Zeng +1
Evaluation of large language models (LLMs) is increasingly critical, yet standard benchmarking methods rely on average accuracy, overlooking both the inherent stochasticity of LLM…
Inertial Quadratic Majorization Minimization with Application to Kernel Regularized Learning
Qiang Heng, Caixing Wang
First-order methods in convex optimization offer low per-iteration cost but often suffer from slow convergence, while second-order methods achieve fast local convergence at the exp…
A Stability Framework for Parameter Selection in the Minimum Covariance Determinant Problem
Qiang Heng, Hui Shen, Kenneth Lange
The Minimum Covariance Determinant (MCD) method is a widely adopted tool for robust estimation and outlier detection. In this paper, we introduce MCD model selection based on the n…