10 citations · 40 across the 27 of their papers we have counts for
9 papers · 1 filter
Monte Carlo Tree Search based Space Transfer for Black-box Optimization
Shukuan Wang, Ke Xue, Lei Song +2
Bayesian optimization (BO) is a popular method for computationally expensive black-box optimization. However, traditional BO methods need to solve new problems from scratch, leadin…
Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer
Ke Xue, Ruo-Tong Chen, Xi Lin +4
In modern chip design, placement aims at placing millions of circuit modules, which is an essential step that significantly influences power, performance, and area (PPA) metrics. R…
Offline Model-Based Optimization by Learning to Rank
Rong-Xi Tan, Ke Xue, Shen-Huan Lyu +5
Offline model-based optimization (MBO) aims to identify a design that maximizes a black-box function using only a fixed, pre-collected dataset of designs and their corresponding sc…
Neural Solver Selection for Combinatorial Optimization
Chengrui Gao, Haopu Shang, Ke Xue +1
Machine learning has increasingly been employed to solve NP-hard combinatorial optimization problems, resulting in the emergence of neural solvers that demonstrate remarkable perfo…
Quality-Diversity with Limited Resources
Ren-Jian Wang, Ke Xue, Cong Guan +1
Quality-Diversity (QD) algorithms have emerged as a powerful optimization paradigm with the aim of generating a set of high-quality and diverse solutions. To achieve such a challen…
Offline Multi-Objective Optimization
Ke Xue, Rong-Xi Tan, Xiaobin Huang +1
Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications. In addition to the objective function being black-box and expe…
Escaping Local Optima in Global Placement
Ke Xue, Xi Lin, Yunqi Shi +3
Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics. Recent advancements in analytical methods, such as DREAMPlace, have demonst…
Reinforced In-Context Black-Box Optimization
Lei Song, Chenxiao Gao, Ke Xue +5
Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular co…
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization
Chao Qian, Ke Xue, Ren-Jian Wang
Quality-Diversity (QD) algorithms are a new type of Evolutionary Algorithms (EAs), aiming to find a set of high-performing, yet diverse solutions. They have found many successful a…