101 citations · 108 across the 4 of their papers we have counts for
10 papers
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…