Search Results for author:
Found 22 papers, 5 papers with code
Constrained Reweighting of Distributions: an Optimal Transport Approach
The key idea is to ensure that the maximum entropy weight adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric while allowing for subtle departures.
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
Both the sub-Gaussian and exponential family models satisfy our general conditions on the reward distribution.
Memory Efficient And Minimax Distribution Estimation Under Wasserstein Distance Using Bayesian Histograms
This result holds for the posterior mean histogram and with respect to posterior contraction: under the class of Borel probability measures and some classes of smooth densities.
On the Convergence of Coordinate Ascent Variational Inference
As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming more and more popular for approximating intractable posterior distributions in large-scale Bayesian models due to its comparable efficacy and superior efficiency.
Fair Clustering via Hierarchical Fair-Dirichlet Process
The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness.
Robust probabilistic inference via a constrained transport metric
The proposed approach finds applications in a wide variety of robust inference problems, where we intend to perform inference on the parameters associated with the centering distribution in presence of outliers.
Factorized Fusion Shrinkage for Dynamic Relational Data
Comparing estimated latent factors involves both adjacent and long-term comparisons, with the time range of comparison considered as a variable.
Structured Optimal Variational Inference for Dynamic Latent Space Models
We consider a latent space model for dynamic networks, where our objective is to estimate the pairwise inner products of the latent positions.
Off-Policy Evaluation Using Information Borrowing and Context-Based Switching
We consider the off-policy evaluation (OPE) problem in contextual bandits, where the goal is to estimate the value of a target policy using the data collected by a logging policy.
Statistical optimality and stability of tangent transform algorithms in logit models
We demonstrate that these assumptions can be completely relaxed if one considers a slight variation of the algorithm by raising the likelihood to a fractional power.
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
More recently, transformation-based models have been used in variational inference (VI) to construct flexible implicit families of variational distributions.
Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting
We provide statistical guarantees for Bayesian variational boosting by proposing a novel small bandwidth Gaussian mixture variational family.
Tail-adaptive Bayesian shrinkage
In this paper, we propose a robust sparse estimation method under diverse sparsity regimes, which has a tail-adaptive shrinkage property.
Revisiting the proton-radius problem using constrained Gaussian processes
We have shown the impact of the physical constraints imposed on the form factor and of the range of experimental data used.
Nuclear Theory Nuclear Experiment Applications
On Statistical Optimality of Variational Bayes
The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation.
$α$-Variational Inference with Statistical Guarantees
We propose a family of variational approximations to Bayesian posterior distributions, called $\alpha$-VB, with provable statistical guarantees.
Frequentist coverage and sup-norm convergence rate in Gaussian process regression
By developing a comparison inequality between two GPs, we provide exact characterization of frequentist coverage probabilities of Bayesian point-wise credible intervals and simultaneous credible bands of the regression function.
Adaptive posterior convergence rates in non-linear latent variable models
In this article, we study rates of posterior contraction in univariate density estimation for a class of non-linear latent variable models where unobserved U(0, 1) latent variables are related to the response variables via a random non-linear regression with an additive error.
Statistics Theory Statistics Theory
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood
In this article, we investigate large sample properties of model selection procedures in a general Bayesian framework when a closed form expression of the marginal likelihood function is not available or a local asymptotic quadratic approximation of the log-likelihood function does not exist.
A Divide and Conquer Strategy for High Dimensional Bayesian Factor Models
Our approach is novel in this regard: it includes all of the $n$ samples in each subproblem and, instead, splits the dimension $p$ into smaller subsets for each subproblem.
Methodology
Sparse additive Gaussian process with soft interactions
Additive nonparametric regression models provide an attractive tool for variable selection in high dimensions when the relationship between the response and predictors is complex.
Bayesian Clustering of Shapes of Curves
The elastic-inner product matrix obtained from the data is modeled using a Wishart distribution whose parameters are assigned carefully chosen prior distributions to allow for automatic inference on the number of clusters.
