Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Langevin Monte Carlo without smoothness   Langevin Monte Carlo (LMC) is an iterative algorithm used to generate samples
from a distribution that is known only up to a normalizing constant. The
nonasymptotic dependence of its mixing time on the dimension and target
accuracy is understood mainly in the setting of smooth (gradient-Lipschitz)
log-densities, a serious limitation for applications in machine learning. In
this paper, we remove this limitation, providing polynomial-time convergence
guarantees for a variant of LMC in the setting of nonsmooth log-concave
distributions. At a high level, our results follow by leveraging the implicit
smoothing of the log-density that comes from a small Gaussian perturbation that
we add to the iterates of the algorithm and controlling the bias and variance
that are induced by this perturbation.

31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn