CleanML: A Study for Evaluating the Impact of Data Cleaning on ML
Classification Tasks

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CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks

20 April 2019

Peng Li

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Papers citing "CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks"

7 / 7 papers shown

Title
Don't Use Large Mini-Batches, Use Local SGD Tao R. Lin Sebastian U. Stich Kumar Kshitij Patel Martin Jaggi 119 432 0 22 Aug 2018
Impacts of Dirty Data: and Experimental Evaluation Zhixin Qi Hongzhi Wang Jianzhong Li Hong Gao 30 17 0 16 Mar 2018
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian Ce Zhang Huan Zhang Cho-Jui Hsieh Wei Zhang Ji Liu 50 1,233 0 25 May 2017
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding Dan Alistarh Demjan Grubic Jerry Li Ryota Tomioka Milan Vojnović MQ 80 423 0 07 Oct 2016
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 812 39,062 0 09 Mar 2016
Staleness-aware Async-SGD for Distributed Deep Learning Wei Zhang Suyog Gupta Xiangru Lian Ji Liu 75 266 0 18 Nov 2015
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Feng Niu Benjamin Recht Christopher Ré Stephen J. Wright 201 2,274 0 28 Jun 2011

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