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The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning
We apply the NDIS theorem to derive DP mechanisms for queries with normally-distributed outputs -- i. e., Gaussian Random Projections (RP) -- and for more general queries -- i. e., Ordinary Least Squares (OLS).
City Metro Network Expansion with Reinforcement Learning
In this method, we formulate the metro expansion as a process of sequential station selection, and design feasibility rules based on the selected station sequence to ensure the reasonable connection patterns of metro line.
A Comparison of First-order Algorithms for Machine Learning
Using an optimization algorithm to solve a machine learning problem is one of mainstreams in the field of science.
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