Mathematical & Statistical Methods
Proximal SCOPE for Distributed Sparse Learning
Shenyi Zhao, Gong-Duo Zhang, Ming-Wei Li, Wu-Jun Li
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GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney
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Cost Effective Active Search
Shali Jiang, Roman Garnett, Benjamin Moseley
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Epidemic Learning: Boosting Decentralized Learning with Randomized Communication Martijn de V os
We discuss these works in more detail in Section 5. Authors are listed in alphabetical order.
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Testing for Families of Distributions via the Fourier Transform
Alistair Stewart, Ilias Diakonikolas, Clement Canonne
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Sampling from Structured Log-Concave Distributions via a Soft-Threshold Dikin Walk Oren Mangoubi Worcester Polytechnic Institute Nisheeth K. Vishnoi Yale University
Interest in this problem derives from its applications to Bayesian inference and differential privacy.
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Deep Poisson gamma dynamical systems
Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou
Moreover, we develop stochastic gradient MCMC inference that is scalable to very long multivariate count time series.
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Communication Compression for Decentralized Training
Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu
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Faster Linear Algebra for Distance Matrices
Such complexities are prohibitive for scaling to large datasets.
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