1 citations · 1 across the 7 of their papers we have counts for
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Gaussian Process Limit Reveals Structural Benefits of Graph Transformers
Nil Ayday, Lingchu Yang, Debarghya Ghoshdastidar
Graph transformers are the state-of-the-art for learning from graph-structured data and are empirically known to avoid several pitfalls of message-passing architectures. However, t…
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…
Nonparametric Kernel Clustering with Bandit Feedback
Victor Thuot, Sebastian Vogt, Debarghya Ghoshdastidar +1
Clustering with bandit feedback refers to the problem of partitioning a set of items, where the clustering algorithm can sequentially query the items to receive noisy observations.…
Tight PAC-Bayesian Risk Certificates for Contrastive Learning
Anna Van Elst, Debarghya Ghoshdastidar
Contrastive representation learning is a modern paradigm for learning representations of unlabeled data via augmentations -- precisely, contrastive models learn to embed semantical…
Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders
Jonghyun Ham, Maximilian Fleissner, Debarghya Ghoshdastidar
Modern deep neural networks exhibit strong generalization even in highly overparameterized regimes. Significant progress has been made to understand this phenomenon in the context…