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09/19/2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Gaussian processes are used in many machine learning applications that r...
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06/09/2023
Semiparametric posterior corrections
We present a new approach to semiparametric inference using corrected po...
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12/01/2022
Scalable Variational Bayes methods for Hawkes processes
Multivariate Hawkes processes are temporal point processes extensively a...
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08/04/2022
A flexible, random histogram kernel for discrete-time Hawkes processes
Hawkes processes are a self-exciting stochastic process used to describe...
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05/31/2022
Estimating a density near an unknown manifold: a Bayesian nonparametric approach
We study the Bayesian density estimation of data living in the offset of...
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05/11/2022
Evidence estimation in finite and infinite mixture models and applications
Estimating the model evidence - or mariginal likelihood of the data - is...
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03/18/2022
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Bayesian coresets approximate a posterior distribution by building a sma...
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03/11/2022
Efficient Bayesian estimation and use of cut posterior in semiparametric hidden Markov models
We consider the problem of estimation in Hidden Markov models with finit...
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11/12/2021
Wasserstein convergence in Bayesian deconvolution models
We study the reknown deconvolution problem of recovering a distribution ...
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07/02/2021
Asymptotic Analysis of Statistical Estimators related to MultiGraphex Processes under Misspecification
This article studies the asymptotic properties of Bayesian or frequentis...
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05/26/2021
Ideal Bayesian Spatial Adaptation
Many real-life applications involve estimation of curves that exhibit co...
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03/31/2021
Bayesian estimation of nonlinear Hawkes process
Multivariate point processes are widely applied to model event-type data...
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10/24/2020
Stable ResNet
Deep ResNet architectures have achieved state of the art performance on ...
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10/21/2019
Sparse Networks with Core-Periphery Structure
We propose a statistical model for graphs with a core-periphery structur...
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05/31/2019
Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel
Stochastic Gradient Descent (SGD) is widely used to train deep neural ne...
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02/19/2019
On the Impact of the Activation Function on Deep Neural Networks Training
The weight initialization and the activation function of deep neural net...
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05/21/2018
On the Selection of Initialization and Activation Function for Deep Neural Networks
The weight initialization and the activation function of deep neural net...
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02/16/2018