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Found 23 papers, 5 papers with code
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits
We propose a new family of bandit algorithms, that are formulated in a general way based on the Biased Maximum Likelihood Estimation (BMLE) method originally appearing in the adaptive control literature.
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.
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems
Hosting capacity analysis (HCA) examines the amount of DERs that can be safely integrated into the grid and is a challenging task in full generality because there are many possible integration of DERs in foresight.
EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations
We propose EBLIME to explain black-box machine learning models and obtain the distribution of feature importance using Bayesian ridge regression models.
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.
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.
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
To choose the bias-growth rate $\alpha(t)$ in RBMLE, we reveal the nontrivial interplay between $\alpha(t)$ and the regret bound that generally applies in both the Exponential Family as well as the sub-Gaussian/Exponential family bandits.
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection.
Signal Adaptive Variable Selector for the Horseshoe Prior
In this article, we propose a simple method to perform variable selection as a post model-fitting exercise using continuous shrinkage priors such as the popular horseshoe prior.
Methodology
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
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression
We propose an efficient way to sample from a class of structured multivariate Gaussian distributions which routinely arise as conditional posteriors of model parameters that are assigned a conditionally Gaussian prior.
Computation
