Publications

Preprints

• T. T.-K. Lau, W. Li, C. Xu, H. Liu and M. Kolar (2024). Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods. [pdf] [arXiv]

• T. T.-K. Lau, H. Liu and M. Kolar (2024). AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods. [pdf] [arXiv]

• T. T.-K. Lau and H. Liu (2022). Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters. [pdf] [arXiv]

Refereed Book Chapters

• T. T.-K. Lau, H. Liu and T. Pock (2024). Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms. In Alessandro Benfenati, Tatiana A. Bubba, Federica Porta, and Marco Viola, editors, Advanced Techniques in Optimization for Machine Learning and Imaging (ATOMI 2022), volume 61 of Springer INdAM Series, pages 83–149. Springer, Singapore, Springer INdAM Series. [URL] [pdf] [arXiv] [code]

Refereed Journal Publications

• E. Chouzenoux, T. T.-K. Lau, C. Lefort, and J.-C. Pesquet (2019). Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging. Journal of Mathematical Imaging and Vision, 61(7):1037–1050. [URL] [pdf]

Refereed Conference Publications

• T. T.-K. Lau and H. Liu (2022). Bregman Proximal Langevin Monte Carlo via Bregman–Moreau Envelopes. In Proceedings of the 39th International Conference on Machine Learning (ICML). [abs] [pdf] [arXiv] [code] [slides]

• J. Zeng*, T. T.-K. Lau*, S. Lin, and Y. Yao (2019). Global Convergence of Block Coordinate Descent in Deep Learning. In Proceedings of the 36th International Conference on Machine Learning (ICML). [abs] [pdf] [arXiv] [code] [slides]

• T. T.-K. Lau, E. Chouzenoux, C. Lefort, and J.-C. Pesquet (2018). Optimal Multivariate Gaussian Fitting for PSF Modeling in Two-photon Microscopy. In Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI 2018). [pdf]

*Equal contribution

Refereed Workshop Papers

• T. T.-K. Lau, J. Zeng, B. Wu, and Y. Yao (2018). A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training. In International Conference on Learning Representations (ICLR), Workshop Track. [URL] [arXiv]

• T.K. Lau and Y. Yao (2017). Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics. The 10th NIPS Workshop on Optimization for Machine Learning, NIPS. [pdf] [arXiv] [code]