Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty

no code implementations • 10 Jun 2018 • Hao Henry Zhou, Yunyang Xiong, Vikas Singh

We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons.

Gaussian Processes Variational Inference

Non-parametric Sparse Additive Auto-regressive Network Models

no code implementations • 23 Jan 2018 • Hao Henry Zhou, Garvesh Raskutti

Using a combination of $\beta$ and $\phi$-mixing properties of Markov chains and empirical process techniques for reproducing kernel Hilbert spaces (RKHSs), we provide upper bounds on mean-squared error in terms of the sparsity $s$, logarithm of the dimension $\log d$, number of time points $T$, and the smoothness of the RKHSs.

Time Series Time Series Analysis

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications

1 code implementation • ICML 2017 • Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh

Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints.