1 citations · 3 across the 16 of their papers we have counts for
17 papers
Optimizer Geometry Sets the Pace: Spectral Learning Dynamics in Matrix Factorization
Mahalakshmi Sabanayagam, Simon Lucey
Recent successes of matrix- and curvature-based optimizers have renewed interest in how update geometry shapes learning. These methods normalize or precondition updates, changing h…
The Quantization Benefits of Residual-Free Transformers
Yiping Ji, Mahalakshmi Sabanayagam, Peyman Moghadam +2
Large-scale transformer training and deployment are increasingly constrained by the transfer of activations, gradients, and optimizer states across accelerators. Low-bit quantizati…
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNN) are currently the most popular approach for learning and prediction on graph-structured data and are deployed in various fields, from social network ana…
Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning
Ajinkya Mohgaonkar, Lukas Gosch, Mahalakshmi Sabanayagam +2
Label-flipping attacks, which corrupt training labels to induce misclassifications at inference, remain a major threat to supervised learning models. This drives the need for robus…
Robustness Certificates for Neural Networks Against Data Poisoning and Evasion Attacks
Sara Taheri, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar +1
The increasing use of machine learning in safety-critical domains amplifies the risk of adversarial threats, especially data poisoning attacks that corrupt training data to degrade…
Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNNs) have become the standard method for learning from networks across fields ranging from biology to social systems, yet a principled understanding of what…
Generalization Certificates for Adversarially Robust Bayesian Linear Regression
Mahalakshmi Sabanayagam, Russell Tsuchida, Cheng Soon Ong +1
Adversarial robustness of machine learning models is critical to ensuring reliable performance under data perturbations. Recent progress has been on point estimators, and this pape…
Cluster Specific Representation Learning
Mahalakshmi Sabanayagam, Omar Al-Dabooni, Pascal Esser
Representation learning aims to extract meaningful lower-dimensional embeddings from data, known as representations. Despite its widespread application, there is no established def…
Exact Certification of (Graph) Neural Networks Against Label Poisoning
Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann +1
Machine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustn…
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar +1
Generalization of machine learning models can be severely compromised by data poisoning, where adversarial changes are applied to the training data. This vulnerability has led to i…
Kernels, Data & Physics
Francesco Cagnetta, Deborah Oliveira, Mahalakshmi Sabanayagam +2
Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches. The notes discuss the so-called NTK appr…
Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections
Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet +1
Test-time defenses are used to improve the robustness of deep neural networks to adversarial examples during inference. However, existing methods either require an additional train…
Unveiling the Hessian's Connection to the Decision Boundary
Mahalakshmi Sabanayagam, Freya Behrens, Urte Adomaityte +1
Understanding the properties of well-generalizing minima is at the heart of deep learning research. On the one hand, the generalization of neural networks has been connected to the…
Improved Representation Learning Through Tensorized Autoencoders
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam +1
The central question in representation learning is what constitutes a good or meaningful representation. In this work we argue that if we consider data with inherent cluster struct…
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel
Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar
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 c…
Graphon based Clustering and Testing of Networks: Algorithms and Theory
Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Network-valued data are encountered in a wide range of applications and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical exa…
New Insights into Graph Convolutional Networks using Neural Tangent Kernels
Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data. Although empirically successful, GCNs exhibit certain behaviour that has…