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05/29/2023
A Bayesian sparse factor model with adaptive posterior concentration
In this paper, we propose a new Bayesian inference method for a high-dim...
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05/24/2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Bayesian approaches for learning deep neural networks (BNN) have been re...
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02/16/2023
Intrinsic and extrinsic deep learning on manifolds
We propose extrinsic and intrinsic deep neural network architectures as ...
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06/16/2022
The convergent Indian buffet process
We propose a new Bayesian nonparametric prior for latent feature models,...
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06/02/2022
Masked Bayesian Neural Networks : Computation and Optimality
As data size and computing power increase, the architectures of deep neu...
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02/07/2022
SLIDE: a surrogate fairness constraint to ensure fairness consistency
As they have a vital effect on social decision makings, AI algorithms sh...
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02/07/2022
Learning fair representation with a parametric integral probability metric
As they have a vital effect on social decision-making, AI algorithms sho...
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09/07/2021
Adaptive variational Bayes: Optimality, computation and applications
In this paper, we explore adaptive inference based on variational Bayes....
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07/17/2020
Optimal Bayesian estimation of Gaussian mixtures with growing number of components
We study posterior concentration properties of Bayesian procedures for e...
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03/26/2020
Nonconvex sparse regularization for deep neural networks and its optimality
Recent theoretical studies proved that deep neural network (DNN) estimat...
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06/17/2019
Smooth function approximation by deep neural networks with general activation functions
There has been a growing interest in expressivity of deep neural network...
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12/10/2018