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Found 9 papers, 0 papers with code
Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
Our statistical bounds depend on the logging policy through its mixing properties.
Distributed Sparse Regression via Penalization
First, we establish statistical consistency of the estimator: under a suitable choice of the penalty parameter, the optimal solution of the penalized problem achieves near optimal minimax rate $\mathcal{O}(s \log d/N)$ in $\ell_2$-loss, where $s$ is the sparsity value, $d$ is the ambient dimension, and $N$ is the total sample size in the network -- this matches centralized sample rates.
Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency
Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic optimization problems.
PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models
We present a PAC-Bayesian analysis of variational Bayes (VB) approximations to tempered Bayesian posterior distributions, bounding the model risk of the VB approximations.
Statistics Theory Statistics Theory
Variational inference for diffusion modulated Cox processes
This paper proposes a stochastic variational inference (SVI) method for computing an approximate posterior path measure of a Cox process.
Estimating Stochastic Poisson Intensities Using Deep Latent Models
We present methodology for estimating the stochastic intensity of a doubly stochastic Poisson process.
Variational Bayesian Methods for Stochastically Constrained System Design Problems
We study system design problems stated as parameterized stochastic programs with a chance-constraint set.
Asymptotic Consistency of Loss-Calibrated Variational Bayes
We also establish the asymptotic consistency of decision rules obtained from a `naive' variational Bayesian procedure.
Asymptotic Consistency of $α-$Rényi-Approximate Posteriors
We study the asymptotic consistency properties of $\alpha$-R\'enyi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with a member of a tractable family of distributions, the member chosen to minimize the $\alpha$-R\'enyi divergence from the true posterior.
