5 papers · 1 filter
On the minimax-rate optimality of approximate Bayesian computation in nonparametric problems
Hien Duy Nguyen
Approximate Bayesian computation (ABC) replaces likelihood evaluation by simulation and comparison of observed and synthetic data. We establish minimax-rate guarantees for nonparam…
Consistency of variational approximations under bounded Kullback--Leibler divergence
Hien Duy Nguyen, Jacob Westerhout, Thomas Guilmeau +1
Variational methods are widely used to approximate posterior distributions in Bayesian inference when exact computation is infeasible. We study when such approximations inherit pos…
On rates of convergence for sample average approximations without smoothness
Hien Duy Nguyen, Jacob Westerhout, Xin Guo
Sample average approximation (SAA) replaces an intractable expected objective by an empirical average and is a basic device of modern stochastic optimization. We develop a rate the…
Revisiting Incremental Stochastic Majorization-Minimization Algorithms with Applications to Mixture of Experts
TrungKhang Tran, TrungTin Nguyen, Gersende Fort +5
Processing high-volume, streaming data is increasingly common in modern statistics and machine learning, where batch-mode algorithms are often impractical because they require repe…
Statistical process control via -values
Hien Duy Nguyen, Dan Wang
We study statistical process control (SPC) through charting of -values. When in control (IC), any valid sequence is super-uniform, a requirement that can hold in n…