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Found 9 papers, 5 papers with code
Exact Certification of (Graph) Neural Networks Against Label Poisoning
Machine learning models are highly vulnerable to label flipping, i. e., the adversarial modification (poisoning) of training labels to compromise performance.
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
Generalization of machine learning models can be severely compromised by data poisoning, where adversarial changes are applied to the training data.
Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections
Test-time defenses are used to improve the robustness of deep neural networks to adversarial examples during inference.
Kernels, Data & Physics
Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches.
Unveiling the Hessian's Connection to the Decision Boundary
Based on this finding, we provide a new and straightforward approach to studying the complexity of a high-dimensional decision boundary; show that this connection naturally inspires a new generalization measure; and finally, we develop a novel margin estimation technique which, in combination with the generalization measure, precisely identifies minima with simple wide-margin boundaries.
Improved Representation Learning Through Tensorized Autoencoders
The central question in representation learning is what constitutes a good or meaningful representation.
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel
The fundamental principle of Graph Neural Networks (GNNs) is to exploit the structural information of the data by aggregating the neighboring nodes using a `graph convolution' in conjunction with a suitable choice for the network architecture, such as depth and activation functions.
New Insights into Graph Convolutional Networks using Neural Tangent Kernels
This paper focuses on semi-supervised learning on graphs, and explains the above observations through the lens of Neural Tangent Kernels (NTKs).
Graphon based Clustering and Testing of Networks: Algorithms and Theory
Using the proposed graph distance, we present two clustering algorithms and show that they achieve state-of-the-art results.
