Domain adaptation (DA) is a statistical learning problem that arises whe...
We analyze the mixing time of Metropolized Hamiltonian Monte Carlo (HMC)...
We study the mixing time of Metropolis-Adjusted Langevin algorithm (MALA...
We analyze the hit-and-run algorithm for sampling uniformly from an isot...
Two recent and seemingly-unrelated techniques for proving mixing bounds ...
We study the mixing time of the Metropolis-adjusted Langevin algorithm (...
Domain adaptation (DA) arises as an important problem in statistical mac...
Hamiltonian Monte Carlo (HMC) is a state-of-the-art Markov chain Monte C...
Optimization algorithms and Monte Carlo sampling algorithms have provide...
The overall performance or expected excess risk of an iterative machine
...
We consider the problem of sampling from a strongly log-concave density ...
We propose and analyze two new MCMC sampling algorithms, the Vaidya walk...
Recently, a novel family of biologically plausible online algorithms for...
We revisit a pioneer unsupervised learning technique called archetypal
a...