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Found 10 papers, 2 papers with code
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.
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.
Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data
We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph.
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
To accelerate and stabilize the convergence of sparse training, we analyze the gradient changes and develop an adaptive gradient correction method.
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.
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.
Estimation of COVID-19 spread curves integrating global data and borrowing information
In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries.
Applications Methodology
Directionally Dependent Multi-View Clustering Using Copula Model
Motivated by this, we propose a copula-based multi-view clustering model where a copula enables the model to accommodate the directional dependence existing in the datasets.
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
