Can We Do Better Than Random Start? The Power of Data Outsourcing
Yi Chen, Jing Dong, Xin T. Tong

TL;DR
This paper explores how data outsourcing can improve model initialization in machine learning, proposing algorithms that outperform random starts under data sharing constraints, with theoretical guarantees and empirical validation.
Contribution
It introduces simulation-based algorithms leveraging outsourced data to find better initial points for optimization, surpassing random start methods.
Findings
Algorithms outperform random start in experiments
Theoretical guarantees for high-probability success
Effective under data sharing constraints
Abstract
Many organizations have access to abundant data but lack the computational power to process the data. While they can outsource the computational task to other facilities, there are various constraints on the amount of data that can be shared. It is natural to ask what can data outsourcing accomplish under such constraints. We address this question from a machine learning perspective. When training a model with optimization algorithms, the quality of the results often relies heavily on the points where the algorithms are initialized. Random start is one of the most popular methods to tackle this issue, but it can be computationally expensive and not feasible for organizations lacking computing resources. Based on three different scenarios, we propose simulation-based algorithms that can utilize a small amount of outsourced data to find good initial points accordingly. Under suitable…
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Taxonomy
TopicsData Quality and Management · Big Data and Business Intelligence · Mobile Crowdsensing and Crowdsourcing
