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Found 14 papers, 6 papers with code
Differential Privacy of Noisy (S)GD under Heavy-Tailed Perturbations
Injecting heavy-tailed noise to the iterates of stochastic gradient descent (SGD) has received increasing attention over the past few years.
Differentially Private Distributed Bayesian Linear Regression with MCMC
We develop a novel generative statistical model for privately shared statistics, which exploits a useful distributional relation between the summary statistics of linear regression.
Differentially Private Online Bayesian Estimation With Adaptive Truncation
We propose a novel online and adaptive truncation method for differentially private Bayesian online estimation of a static parameter regarding a population.
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm
Our MCMC-based RL algorithm is a model-free and gradient-free algorithm, in which the designer does not have to know the precise dynamics of the plant and its uncertainties.
Statistic Selection and MCMC for Differentially Private Bayesian Estimation
This paper concerns differentially private Bayesian estimation of the parameters of a population distribution, when a statistic of a sample from that population is shared in noise to provide differential privacy.
Learning with Subset Stacking
We call this algorithm ``LEarning with Subset Stacking'' or LESS, due to its resemblance to the method of stacking regressors.
Differentially Private Accelerated Optimization Algorithms
The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy.
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs
We believe that the Bayesian approach to causal discovery both allows the rich methodology of Bayesian inference to be used in various difficult aspects of this problem and provides a unifying framework to causal discovery research.
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns
We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the topic models such as the latent Dirichlet allocation.
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors
In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation.
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors
With a statistical view, addressing these issues would involve the problem of characterizing the posterior densities of the shapes of the objects to be segmented.
Bayesian tracking and parameter learning for non-linear multiple target tracking models
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models.
An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models
In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters.
Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo
We propose sequential Monte Carlo based algorithms for maximum likelihood estimation of the static parameters in hidden Markov models with an intractable likelihood using ideas from approximate Bayesian computation.
Computation Methodology
