
Adaptive Multilevel Hypergradient Descent
Adaptive learning rates can lead to faster convergence and better final ...
read it

A Bayesian Long ShortTerm Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
ValueatRisk (VaR) and Expected Shortfall (ES) are widely used in the f...
read it

Assessment and adjustment of approximate inference algorithms using the law of total variance
A common method for assessing validity of Bayesian sampling or approxima...
read it

Spectral Subsampling MCMC for Stationary Time Series
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datase...
read it

Identifying relationships between cognitive processes across tasks, contexts, and time
It is commonly assumed that a specific testing occasion (task, design, p...
read it

Variational Bayes on Manifolds
Variational Bayes (VB) has become a versatile tool for Bayesian inferenc...
read it

Timeevolving psychological processes over repeated decisions
Many psychological experiments have participants repeat a simple task. T...
read it

Robustly estimating the marginal likelihood for cognitive models via importance sampling
Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of B...
read it

A long shortterm memory stochastic volatility model
Stochastic Volatility (SV) models are widely used in the financial secto...
read it

Manifold Optimisation Assisted Gaussian Variational Approximation
Variational approximation methods are a way to approximate the posterior...
read it

Subsampling MCMC  An introduction for the survey statistician
The rapid development of computing power and efficient Markov Chain Mont...
read it

Subsampling MCMC  A review for the survey statistician
The rapid development of computing power and efficient Markov Chain Mont...
read it

Adversarial Robustness Toolbox v0.2.2
Adversarial examples have become an indisputable threat to the security ...
read it

New Estimation Approaches for the Linear Ballistic Accumulator Model
The Linear Ballistic Accumulator (LBA) model of Brown (2008) is used as ...
read it

Bayesian Deep Net GLM and GLMM
Deep feedforward neural networks (DFNNs) are a powerful tool for functio...
read it

Subsampling Sequential Monte Carlo for Static Bayesian Models
Our article shows how to carry out Bayesian inference by combining data ...
read it

Flexible Density Tempering Approaches for State Space Models with an Application to Factor Stochastic Volatility Models
Duan (2015) propose a tempering or annealing approach to Bayesian infere...
read it

Hamiltonian Monte Carlo with Energy Conserving Subsampling
Hamiltonian Monte Carlo (HMC) has recently received considerable attenti...
read it

Exact Subsampling MCMC
Speeding up Markov Chain Monte Carlo (MCMC) for data sets with many obse...
read it

Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework...
read it

Variational approximation for heteroscedastic linear models and matching pursuit algorithms
Modern statistical applications involving large data sets have focused a...
read it

Model Selection by Loss Rank for Classification and Unsupervised Learning
Hutter (2007) recently introduced the loss rank principle (LoRP) as a ge...
read it

The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
Lasso and other regularization procedures are attractive methods for var...
read it
MinhNgoc Tran
is this you? claim profile