no code implementations • 1 Jul 2014 • Huahua Wang, Arindam Banerjee
One is online or stochastic gradient descent (OGD/SGD), and the other is randomzied coordinate descent (RBCD).
no code implementations • NeurIPS 2014 • Huahua Wang, Arindam Banerjee
The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance.
no code implementations • 26 Sep 2013 • Qiang Fu, Huahua Wang, Arindam Banerjee
We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM).
no code implementations • 17 Jun 2013 • Huahua Wang, Arindam Banerjee
Online optimization has emerged as powerful tool in large scale optimization.
no code implementations • NeurIPS 2014 • Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
In this paper, we propose a parallel randomized block coordinate method named Parallel Direction Method of Multipliers (PDMM) to solve the optimization problems with multi-block linear constraints.
no code implementations • NeurIPS 2013 • Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
We consider the problem of sparse precision matrix estimation in high dimensions using the CLIME estimator, which has several desirable theoretical properties.
no code implementations • NeurIPS 2012 • Shiva P. Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville
Given their pervasive use, social media, such as Twitter, have become a leading source of breaking news.
no code implementations • 3 Jun 2020 • Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, Alyssa Dayan, Sidney Zhang, Brian C. Becker, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.