11 papers
Local and Global Contraction Principles for MCMC Mixing
Alireza Daeijavad, Shahab Asoodeh
We develop a contraction-based framework for proving mixing-time bounds for Markov chain Monte Carlo algorithms. The framework is built around global and local contraction coeffici…
Breaking the Finite-Sample Barrier in Entropy Coupling
Shahab Asoodeh, Jun Chen
Dependence among marginally constrained observations can break a finite-sample barrier. To formalize this phenomenon, we introduce the \emph{minimum list entropy coupling} $H(P\|Q_…
PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
Wei Dong, Han Zhou, Terry Ji +6
Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training often yields overly smooth res…
Multi-Marginal Couplings for Metropolis-Hastings
Buu Phan, Gergely Flamich, Ashish Khisti +1
Convergence diagnosis for Markov chain Monte Carlo is a matter of fundamental importance in computational statistics: it determines the resources allocated to a particular sampling…
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity
Alireza F. Pour, Hassan Ashtiani, Shahab Asoodeh
We study the problem of hypothesis selection under the constraint of local differential privacy. Given a class of distributions and a set of i.i.d. samples from a…
Optimal Fairness under Local Differential Privacy
Hrad Ghoukasian, Shahab Asoodeh
We investigate how to optimally design local differential privacy (LDP) mechanisms that reduce data unfairness and thereby improve fairness in downstream classification. We first d…