Near-Optimal Comparison Based Clustering. - Michaël Perrot; Pascal Mattia Esser; Debarghya Ghoshdastidar (NeurIPS, 2020)

Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. - Pascal Mattia Esser; Leena C. Vankadara; Debarghya Ghoshdastidar (NeurIPS, 2021)

New Insights into Graph Convolutional Networks using Neural Tangent Kernels. - Mahalakshmi Sabanayagam; Pascal Mattia Esser; Debarghya Ghoshdastidar (CoRR, 2021)

Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds. - Pascal Mattia Esser; Frank Nielsen (CoRR, 2021)

On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models. - Pascal Mattia Esser; Frank Nielsen (CoRR, 2022)

Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis. - Mahalakshmi Sabanayagam; Pascal Mattia Esser; Debarghya Ghoshdastidar (CoRR, 2022)

Improved Representation Learning Through Tensorized Autoencoders. - Pascal Mattia Esser; Satyaki Mukherjee; Mahalakshmi Sabanayagam; Debarghya Ghoshdastidar (AISTATS, 2023)

Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel. - Mahalakshmi Sabanayagam; Pascal Mattia Esser; Debarghya Ghoshdastidar (Trans. Mach. Learn. Res., 2023)

Non-parametric Representation Learning with Kernels. - Pascal Mattia Esser; Maximilian Fleissner; Debarghya Ghoshdastidar (AAAI, 2024)

Cluster Specific Representation Learning. - Mahalakshmi Sabanayagam; Omar Al-Dabooni; Pascal Mattia Esser (CoRR, 2024)

Representation Learning Dynamics of Self-Supervised Models. - Pascal Mattia Esser; Satyaki Mukherjee; Debarghya Ghoshdastidar (Trans. Mach. Learn. Res., 2024)

When Can We Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? - Gautham Govind Anil; Pascal Mattia Esser; Debarghya Ghoshdastidar (AAAI, 2025)

A Probabilistic Model for Self-Supervised Learning. - Maximilian Fleissner; Pascal Mattia Esser; Debarghya Ghoshdastidar (CoRR, 2025)