3 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu +2
Modern deep neural networks often require distributed training with many workers due to their large size. As the number of workers increases, communication overheads become the mai…
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
Tim Tsz-Kit Lau, Han Liu, Thomas Pock
We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodali…
Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters
Tim Tsz-Kit Lau, Han Liu
In many applications in statistics and machine learning, the availability of data samples from multiple possibly heterogeneous sources has become increasingly prevalent. On the oth…
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
Tim Tsz-Kit Lau, Jinshan Zeng, Baoyuan Wu +1
Training deep neural networks (DNNs) efficiently is a challenge due to the associated highly nonconvex optimization. The backpropagation (backprop) algorithm has long been the most…