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08/08/2023
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation
Adversarial robustness is a research area that has recently received a l...
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08/08/2023
Improving Performance of Semi-Supervised Learning by Adversarial Attacks
Semi-supervised learning (SSL) algorithm is a setup built upon a realist...
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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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05/23/2023
Covariate balancing using the integral probability metric for causal inference
Weighting methods in causal inference have been widely used to achieve a...
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01/20/2023
Within-group fairness: A guidance for more sound between-group fairness
As they have a vital effect on social decision-making, AI algorithms not...
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01/11/2023
ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models
Identifying whether a given sample is an outlier or not is an important ...
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06/07/2022
Adaptive Regularization for Adversarial Training
Adversarial training, which is to enhance robustness against adversarial...
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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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06/29/2021
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
In many classification problems, collecting massive clean-annotated data...
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05/09/2021
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models
We investigate statistical properties of a likelihood approach to nonpar...
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12/04/2020
Kernel-convoluted Deep Neural Networks with Data Augmentation
The Mixup method (Zhang et al. 2018), which uses linearly interpolated d...
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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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09/15/2019
Understanding and Improving Virtual Adversarial Training
In semi-supervised learning, virtual adversarial training (VAT) approach...
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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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12/21/2018
Primal path algorithm for compositional data analysis
Compositional data have two unique characteristics compared to typical m...
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12/10/2018
Fast convergence rates of deep neural networks for classification
We derive the fast convergence rates of a deep neural network (DNN) clas...
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12/02/2018
