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5 papers · 2 filters
On Connecting Stochastic Gradient MCMC and Differential Privacy
Bai Li, Changyou Chen, Hao Liu +1
Significant success has been realized recently on applying machine learning to real-world applications. There have also been corresponding concerns on the privacy of training data,…
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, Xuejun Liao, Lawrence Carin
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate v…
Rates of Convergence of Spectral Methods for Graphon Estimation
Jiaming Xu
This paper studies the problem of estimating the grahpon model - the underlying generating mechanism of a network. Graphon estimation arises in many applications such as predicting…
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Joseph Futoma, Sanjay Hariharan, Katherine Heller
We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from in…
Stochastic Gradient Monomial Gamma Sampler
Yizhe Zhang, Changyou Chen, Zhe Gan +2
Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when t…