69 citations · 116 across the 31 of their papers we have counts for
40 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…
Learning from Totally Unpaired Data via Minimum Conditional Entropy Coupling
Shahab Asoodeh, Cheuk Ting Li, Jun Chen
Consider an "unsupervised classification" setting, where the goal is to learn a predictor of the categorical class variable using the categorical feature variables $Y_1,\ldots,…
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…
Optimality of Staircase Mechanisms for Vector Queries under Differential Privacy
James Melbourne, Mario Diaz, Shahab Asoodeh
We study the optimal design of additive mechanisms for vector-valued queries under -differential privacy (DP). Given only the sensitivity of a query and a norm-monotone cost fun…
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…