research
∙
03/30/2023
Coskewness under dependence uncertainty
We study the impact of dependence uncertainty on the expectation of the ...
share
research
∙
01/19/2023
On the Vulnerability of Backdoor Defenses for Federated Learning
Federated Learning (FL) is a popular distributed machine learning paradi...
research
∙
10/02/2022
Spectral Augmentation for Self-Supervised Learning on Graphs
Graph contrastive learning (GCL), as an emerging self-supervised learnin...
research
∙
09/29/2022
How Powerful is Implicit Denoising in Graph Neural Networks
Graph Neural Networks (GNNs), which aggregate features from neighbors, a...
research
∙
07/10/2022
One-shot Neural Backdoor Erasing via Adversarial Weight Masking
Recent studies show that despite achieving high accuracy on a number of ...
research
∙
06/01/2022
RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model
Counterfactual (CF) explanations for machine learning (ML) models are pr...
research
∙
05/05/2022
Communication-Efficient Adaptive Federated Learning
Federated learning is a machine learning training paradigm that enables ...
research
∙
03/15/2022
Do Language Models Plagiarize?
Past literature has illustrated that language models do not fully unders...
research
∙
02/03/2022
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations
Owing much to the revolution of information technology, the recent progr...
research
∙
12/31/2021
Benign Overfitting in Adversarially Robust Linear Classification
"Benign overfitting", where classifiers memorize noisy training data yet...
research
∙
11/01/2021
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
Due to the explosion in the size of the training datasets, distributed l...
research
∙
08/18/2021
A New Asymmetric Copula with Reversible Correlations and Its Application to the EU Sovereign Debt Crisis
This paper proposes a novel asymmetric copula based upon bivariate split...
research
∙
10/03/2020
Does Network Width Really Help Adversarial Robustness?
Adversarial training is currently the most powerful defense against adve...
research
∙
10/03/2020
Efficient Robust Training via Backward Smoothing
Adversarial training is so far the most effective strategy in defending ...
research
∙
06/23/2020
RayS: A Ray Searching Method for Hard-label Adversarial Attack
Deep neural networks are vulnerable to adversarial attacks. Among differ...
research
∙
03/01/2020
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Starting with Gilmer et al. (2018), several works have demonstrated the ...
research
∙
11/27/2018
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Depending on how much information an adversary can access to, adversaria...
research
∙
06/18/2018
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Adaptive gradient methods, which adopt historical gradient information t...
research
∙
07/20/2017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
We present a unified framework to analyze the global convergence of Lang...
research
∙
04/20/2017
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
We consider the phase retrieval problem of recovering the unknown signal...
research
∙
06/02/2016
