Abstract Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an
important technique for accelerating the convergence of the conventional Markov Chain …

We propose a novel adaptive empirical Bayesian (AEB) method for sparse deep learning,
where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The …

We propose an adaptively weighted stochastic gradient Langevin dynamics (AWSGLD)
algorithm for Bayesian learning of big data problems. The proposed algorithm is scalable …

We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm
(SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian …

We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification
and mean predictions with distributed clients. In particular, we generalize beyond normal …

Abstract The Langevin Markov chain algorithms are widely deployed methods to sample
from distributions in challenging high-dimensional and non-convex statistics and machine …

Hamiltonian Monte Carlo (HMC) is a very popular and generic collection of Markov chain
Monte Carlo (MCMC) algorithms. One explanation for the popularity of HMC algorithms is …

Gradient descent (GD) is known to converge quickly for convex objective functions, but it can
be trapped at local minima. On the other hand, Langevin dynamics (LD) can explore the …

We present a new class of Langevin-based algorithms, which overcomes many of the known
shortcomings of popular adaptive optimizers that are currently used for the fine tuning of …

We study the problem of minimizing a convex function given by a zeroth order oracle that is
possibly corrupted by {\em outlier noise}. Specifically, we assume the function values at …