no code implementations • 5 Jun 2013 • Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias Seeger
Latent Gaussian models (LGMs) are widely used in statistics and machine learning.
no code implementations • 11 Sep 2018 • Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, Pooya Ronagh
We introduce two quantum algorithms for solving structured prediction problems.
no code implementations • ICML 2020 • Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander
Online mirror descent (OMD) and dual averaging (DA) -- two fundamental algorithms for online convex optimization -- are known to have very similar (and sometimes identical) performance guarantees when used with a fixed learning rate.
1 code implementation • 14 Oct 2020 • Zhenan Fan, Halyun Jeong, Babhru Joshi, Michael P. Friedlander
The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components.
no code implementations • 23 Dec 2020 • Michael P. Friedlander, Halyun Jeong, Yaniv Plan, Ozgur Yilmaz
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence.
Information Theory Numerical Analysis Information Theory Numerical Analysis 94-XX
no code implementations • 19 Sep 2021 • Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang
The success of federated learning depends largely on the participation of data owners.
no code implementations • 7 Jan 2022 • Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang
We show that VerFedSV not only satisfies many desirable properties for fairness but is also efficient to compute, and can be adapted to both synchronous and asynchronous vertical federated learning algorithms.
1 code implementation • 26 Jan 2022 • Zhenan Fan, Huang Fang, Michael P. Friedlander
We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coordinate descent method developed by Necora et al.[Journal of Optimization Theory and Applications, 2017].
1 code implementation • 16 Aug 2022 • Zhenan Fan, Zirui Zhou, Jian Pei, Michael P. Friedlander, Jiajie Hu, Chengliang Li, Yong Zhang
Federated learning is an emerging technique for training models from decentralized data sets.