Chenxin Ma

According to our database, Chenxin Ma authored at least 14 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

A Practical End-to-End Inventory Management Model with Deep Learning.

Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun Max Shen

Manag. Sci., February, 2023

2022

Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis.

Yingke Yang, Zhuanghe Tian, Mengyao Song, Chenxin Ma, Zhenyang Ge, Peiluan Li

Entropy, 2022

2021

An accelerated communication-efficient primal-dual optimization framework for structured machine learning.

Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác

Optim. Methods Softw., 2021

Fast and safe: accelerated gradient methods with optimality certificates and underestimate sequences.

Majid Jahani, Naga Venkata C. Gudapati, Chenxin Ma, Rachael Tappenden, Martin Takác

Comput. Optim. Appl., 2021

2020

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy.

Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2017

Distributed optimization with arbitrary local solvers.

Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác

Optim. Methods Softw., 2017

CoCoA: A General Framework for Communication-Efficient Distributed Optimization.

Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi

J. Mach. Learn. Res., 2017

Underestimate Sequences via Quadratic Averaging.

Chenxin Ma, Naga Venkata C. Gudapati, Majid Jahani, Rachael Tappenden, Martin Takác

CoRR, 2017

Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing.

Chenxin Ma, Martin Takác

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption.

Chenxin Ma, Rachael Tappenden, Martin Takác

J. Mach. Learn. Res., 2016

Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing.

Chenxin Ma, Martin Takác

CoRR, 2016

2015

Linear Convergence of the Randomized Feasible Descent Method Under the Weak Strong Convexity Assumption.

Chenxin Ma, Rachael Tappenden, Martin Takác

CoRR, 2015

Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?

Chenxin Ma, Martin Takác

CoRR, 2015

Adding vs. Averaging in Distributed Primal-Dual Optimization.

Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác

Proceedings of the 32nd International Conference on Machine Learning, 2015