5 papers
Privacy-Preserving Black-Box Optimization (PBBO): Theory and the Model-Based Algorithm DFOp
Pengcheng Xie
This paper focuses on solving unconstrained privacy-preserving black-box optimization (PBBO), its corresponding least Frobenius norm updating of quadratic models, and the different…
Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy
Yitong He, Pengcheng Xie
Solving large-scale optimization problems is a bottleneck and is very important for machine learning and multiple kinds of scientific problems. Subspace-based methods using the loc…
Objective Value Change and Shape-Based Accelerated Optimization for the Neural Network Approximation
Pengcheng Xie, Zihao Zhou, Zijian Zhou
This paper introduce a novel metric of an objective function f, we say VC (value change) to measure the difficulty and approximation affection when conducting an neural network app…
Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models
Tai An, Ruwu Cai, Yanzhe Zhang +6
In the era of large language models (LLMs), N:M sparsity has emerged as a structured compression technique critical for accelerating inference. While prior work has primarily focus…
ReMU: Regional Minimal Updating for Model-Based Derivative-Free Optimization
Pengcheng Xie, Stefan M. Wild
Derivative-free optimization (DFO) problems are optimization problems where derivative information is unavailable or extremely difficult to obtain. Model-based DFO solvers have bee…