68 citations · 95 across the 7 of their papers we have counts for
8 papers
Bayesian high-dimensional linear regression with generic spike-and-slab priors
Bai Jiang, Qiang Sun
Spike-and-slab priors are popular Bayesian solutions for high-dimensional linear regression problems. Previous theoretical studies on spike-and-slab methods focus on specific prior…
Adaptive Huber Regression on Markov-dependent Data
Jianqing Fan, Yongyi Guo, Bai Jiang
High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For the convenience of theoretical analyses, classical metho…
Bayesian Factor-adjusted Sparse Regression
Jianqing Fan, Bai Jiang, Qiang Sun
This paper investigates the high-dimensional linear regression with highly correlated covariates. In this setup, the traditional sparsity assumption on the regression coefficients…
Bernstein's inequalities for general Markov chains
Bai Jiang, Qiang Sun, Jianqing Fan
We establish Bernstein's inequalities for functions of general (general-state-space and possibly non-reversible) Markov chains. These inequalities achieve sharp variance proxies an…
Hoeffding's lemma for Markov Chains and its applications to statistical learning
Jianqing Fan, Bai Jiang, Qiang Sun
We extend Hoeffding's lemma to general-state-space and not necessarily reversible Markov chains. Let be a stationary Markov chain with invariant measure and…
Convergence of Contrastive Divergence with Annealed Learning Rate in Exponential Family
Bai Jiang, Tung-yu Wu, Wing H. Wong
In our recent paper, we showed that in exponential family, contrastive divergence (CD) with fixed learning rate will give asymptotically consistent estimates \cite{wu2016convergenc…
Convergence of Contrastive Divergence Algorithm in Exponential Family
Bai Jiang, Tung-Yu Wu, Yifan Jin +1
The Contrastive Divergence (CD) algorithm has achieved notable success in training energy-based models including Restricted Boltzmann Machines and played a key role in the emergenc…
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network
Bai Jiang, Tung-yu Wu, Charles Zheng +1
Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy…