Welcome to Wei-Lin Chiang’s page!
- I am a first-year PhD student in the UC Berkeley RISElab.
- I obtained my bachelor’s and Master’s degree from National Taiwan University under the supervision of Prof. Chih-Jen Lin.
- My research interests include optimization for machine learning, data mining, scalable ML algorithms and its system design.
- I enjoy developing ML softwares and I am always happy to learn how it is being used! Email me if you have questions or find our softwares useful.
- More details can be found in my CV.
- Intern@Google Research, Mountain View (Dec. 2018 - Mar. 2019)
Developing efficient algorithms for training large and deep GCN models
- Intern@Alibaba Group, Hangzhou (July 2017 - Sept. 2017)
Developing distributed ML algorithms on Alibaba’s parameter server (KunPeng)
- Intern@Microsoft Research Asia, Beijing (Dec. 2016 - Feb. 2017)
Investigating distributed training methods on deep learning frameworks
- Intern@Microsoft, Redmond (July 2016 - Oct. 2016)
Developing large-scale ML algorithms on Microsoft’s distributed platform (REEF)
Publications (Google Scholar Profile)
- Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
Yu-Sheng Li, Wei-Lin Chiang, and Ching-pei Lee.
International Conference on Machine Learning (ICML), 2020
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks [code, dataset (Amazon2M)]
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh.
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2019 (Oral) slides, poster
- Preconditioned Conjugate Gradient Methods in Truncated Newton Frameworks for Large-scale Linear Classification [supplement & code. Implementation available in LIBLINEAR after version 2.20.]
Chih-Yang Hsia, Wei-Lin Chiang, and Chih-Jen Lin.
Asian Conference on Machine Learning (ACML), 2018 (Best paper award) slides, poster
- Limited-memory Common-directions Method for Distributed L1-regularized Linear Classification [supplement & code. Implementation available in Distributed LIBLINEAR.]
Wei-Lin Chiang, Yu-Sheng Li, Ching-pei Lee, and Chih-Jen Lin.
SIAM International Conference on Data Mining (SDM), 2018 slides, poster
- Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments [supplement, code. Implementation available in Multi-core LIBLINEAR.]
Wei-Lin Chiang, Mu-Chu Lee, and Chih-Jen Lin.
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2016 poster
- Fast Matrix-vector Multiplications for Large-scale Logistic Regression on Shared-memory Systems [supplement, code. Implementation available in Multi-core LIBLINEAR.]
Mu-Chu Lee, Wei-Lin Chiang, and Chih-Jen Lin.
IEEE International Conference on Data Mining (ICDM), 2015 slides
TensorFlow implementation of an efficient algorithm for large (million-scale) and deep GCN
Achieved state-of-the-art performance on some public datasets (e.g., PPI, Reddit)
- Distributed LIBLINEAR
Distributed extension of a widely-used linear classification package, LIBLINEAR
Developed L1-regularized LR solver for solving large (billion-scale) tasks
- Multi-core LIBLINEAR
Multi-core extension of a widely-used linear classification package, LIBLINEAR
Developed efficient parallel algorithms for primal and dual solvers