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06/27/2023
Adversarial Training for Graph Neural Networks
Despite its success in the image domain, adversarial training does not (...
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01/02/2023
Training Differentially Private Graph Neural Networks with Random Walk Sampling
Deep learning models are known to put the privacy of their training data...
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07/09/2022
On the Robustness and Anomaly Detection of Sparse Neural Networks
The robustness and anomaly detection capability of neural networks are c...
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06/21/2022
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
Pruning, the task of sparsifying deep neural networks, received increasi...
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12/29/2021
Monte Carlo EM for Deep Time Series Anomaly Detection
Time series data are often corrupted by outliers or other kinds of anoma...
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10/26/2021
Robustness of Graph Neural Networks at Scale
Graph Neural Networks (GNNs) are increasingly important given their popu...
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10/26/2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
The interdependence between nodes in graphs is key to improve class pred...
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09/10/2021
A Study of Joint Graph Inference and Forecasting
We study a recent class of models which uses graph neural networks (GNNs...
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05/10/2021
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Uncertainty awareness is crucial to develop reliable machine learning mo...
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03/21/2021
Language-Agnostic Representation Learning of Source Code from Structure and Context
Source code (Context) and its parsed abstract syntax tree (AST; Structur...
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10/29/2020
Reliable Graph Neural Networks via Robust Aggregation
Perturbations targeting the graph structure have proven to be extremely ...
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10/28/2020
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Robustness to adversarial perturbations and accurate uncertainty estimat...
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06/16/2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Accurate estimation of aleatoric and epistemic uncertainty is crucial to...
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10/30/2019
Group Centrality Maximization for Large-scale Graphs
The study of vertex centrality measures is a key aspect of network analy...
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06/28/2019
Certifiable Robustness and Robust Training for Graph Convolutional Networks
Recent works show that Graph Neural Networks (GNNs) are highly non-robus...
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02/22/2019
Adversarial Attacks on Graph Neural Networks via Meta Learning
Deep learning models for graphs have advanced the state of the art on ma...
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05/21/2018
Adversarial Attacks on Neural Networks for Graph Data
Deep learning models for graphs have achieved strong performance for the...
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05/21/2018
Adversarial Attacks on Classification Models for Graphs
Deep learning models for graphs have achieved strong performance for the...
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03/02/2018
