6 citations · 6 across the 15 of their papers we have counts for
4 papers · 1 filter
The Optimality of (Accelerated) SGD for High-Dimensional Quadratic Optimization
Haihan Zhang, Yuanshi Liu, Qianwen Chen +1
Stochastic gradient descent (SGD) is a widely used algorithm in machine learning, particularly for neural network training. Recent studies on SGD for canonical quadratic optimizati…
Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition
Shihong Ding, Long Yang, Luo Luo +1
We study a typical optimization model where the optimization variable is composed of multiple probability distributions. Though the model appears frequently in practice, such as fo…
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
Yexin Zhang, Chenyi Zhang, Cong Fang +2
Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the stud…
On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization
Jiancong Xiao, Ziniu Li, Xingyu Xie +4
Accurately aligning large language models (LLMs) with human preferences is crucial for informing fair, economically sound, and statistically efficient decision-making processes. Ho…