10 citations · 40 across the 27 of their papers we have counts for
20 papers · 1 filter
SHSP: Structure-Aware Hierarchical Solution Prediction for Mixed-Integer Linear Programming
Zherong Zhang, Guanlin Li, Chengrui Gao +5
Mixed-Integer Linear Programming (MILP) is a fundamental optimization paradigm in combinatorial optimization and has been widely applied across real-world domains. Due to its NP-ha…
On the Learnability of Offline Model-Based Optimization: A Ranking Perspective
Shen-Huan Lyu, Rong-Xi Tan, Ke Xue +4
Offline model-based optimization (MBO) seeks to discover high-performing designs using only a fixed dataset of past evaluations. Most existing methods rely on learning a surrogate…
Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
Ming Chen, Sheng Tang, Rong-Xi Tan +4
Decoding-based regression, which reformulates regression as a sequence generation task, has emerged as a promising paradigm of applying large language models for numerical predicti…
Sequential Multi-Agent Dynamic Algorithm Configuration
Chen Lu, Ke Xue, Lei Yuan +4
Dynamic algorithm configuration (DAC) is a recent trend in automated machine learning, which can dynamically adjust the algorithm's configuration during the execution process and r…
Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings
Rong-Xi Tan, Ming Chen, Ke Xue +4
The pursuit of universal black-box optimization (BBO) algorithms is a longstanding goal. However, unlike domains such as language or vision, where scaling structured data has drive…
Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models
Ren-Jian Wang, Ke Xue, Zeyu Qin +7
Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deployment of artificial intelligence.…
Pareto Set Learning for Multi-Objective Reinforcement Learning
Erlong Liu, Yu-Chang Wu, Xiaobin Huang +4
Multi-objective decision-making problems have emerged in numerous real-world scenarios, such as video games, navigation and robotics. Considering the clear advantages of Reinforcem…
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…
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…
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Xiaobin Huang, Lei Song, Ke Xue +1
Bayesian optimization (BO) is a sample-efficient method and has been widely used for optimizing expensive black-box functions. Recently, there has been a considerable interest in B…
Diversity from Human Feedback
Ren-Jian Wang, Ke Xue, Yutong Wang +4
Diversity plays a significant role in many problems, such as ensemble learning, reinforcement learning, and combinatorial optimization. How to define the diversity measure is a lon…
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy
Chengrui Gao, Haopu Shang, Ke Xue +2
Machine learning has been adapted to help solve NP-hard combinatorial optimization problems. One prevalent way is learning to construct solutions by deep neural networks, which has…
Macro Placement by Wire-Mask-Guided Black-Box Optimization
Yunqi Shi, Ke Xue, Lei Song +1
The development of very large-scale integration (VLSI) technology has posed new challenges for electronic design automation (EDA) techniques in chip floorplanning. During this proc…
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Lei Song, Ke Xue, Xiaobin Huang +1
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of…
Multi-agent Dynamic Algorithm Configuration
Ke Xue, Jiacheng Xu, Lei Yuan +4
Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (D…