101 citations · 265 across the 19 of their papers we have counts for
19 papers
Generating Redstone Style Cities in Minecraft
Shuo Huang, Chengpeng Hu, Julian Togelius +1
Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real…
Constrained Reinforcement Learning for Dynamic Material Handling
Chengpeng Hu, Ziming Wang, Jialin Liu +3
As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The imp…
Local Optima Correlation Assisted Adaptive Operator Selection
Jiyuan Pei, Hao Tong, Jialin Liu +2
For solving combinatorial optimisation problems with metaheuristics, different search operators are applied for sampling new solutions in the neighbourhood of a given solution. It…
Games for Artificial Intelligence Research: A Review and Perspectives
Chengpeng Hu, Yunlong Zhao, Ziqi Wang +2
Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision ma…
Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning
Qingquan Zhang, Jialin Liu, Zeqi Zhang +3
In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the traini…
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years. Still, many classes of MILPs qu…
On Representing Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
Learning to optimize is a rapidly growing area that aims to solve optimization problems or improve existing optimization algorithms using machine learning (ML). In particular, the…
Generating Game Levels of Diverse Behaviour Engagement
Keyuan Zhang, Jiayu Bai, Jialin Liu
Recent years, there has been growing interests in experience-driven procedural level generation. Various metrics have been formulated to model player experience and help generate p…
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest K. Ryu, Jialin Liu, Sicheng Wang +3
Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An adv…
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Hao Tong, Jialin Liu, Xin Yao
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in…
Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems
Hao Tong, Changwu Huang, Jialin Liu +1
Very expensive problems are very common in practical system that one fitness evaluation costs several hours or even days. Surrogate assisted evolutionary algorithms (SAEAs) have be…
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Doris Xin, Stephen Macke, Litian Ma +3
Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Un…
Helix: Accelerating Human-in-the-loop Machine Learning
Doris Xin, Litian Ma, Jialin Liu +3
Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model train…
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities
Doris Xin, Litian Ma, Jialin Liu +3
Development of machine learning (ML) workflows is a tedious process of iterative experimentation: developers repeatedly make changes to workflows until the desired accuracy is atta…
PSO-based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application
Chang-Shing Lee, Mei-Hui Wang, Chi-Shiang Wang +4
This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications…
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
Diego Perez-Liebana, Jialin Liu, Ahmed Khalifa +3
General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI)…
Performance Evaluation and Modeling of HPC I/O on Non-Volatile Memory
Wei Liu, Kai Wu, Jialin Liu +2
HPC applications pose high demands on I/O performance and storage capability. The emerging non-volatile memory (NVM) techniques offer low-latency, high bandwidth, and persistence f…
OrpheusDB: Bolt-on Versioning for Relational Databases
Silu Huang, Liqi Xu, Jialin Liu +2
Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system th…
Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints
Mengdi Wang, Yichen Chen, Jialin Liu +1
Consider convex optimization problems subject to a large number of constraints. We focus on stochastic problems in which the objective takes the form of expected values and the fea…