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Found 6 papers, 0 papers with code
Fat Shattering, Joint Measurability, and PAC Learnability of POVM Hypothesis Classes
We show that the empirical risk defined in previous works and matching the definition in the classical theory fails to satisfy the uniform convergence property enjoyed in the classical setting for some learnable classes.
Promise and Limitations of Supervised Optimal Transport-Based Graph Summarization via Information Theoretic Measures
To gain a theoretical perspective on the supervised summarization problem itself, we first formulate it in terms of maximizing the Shannon mutual information between the summarized graph and the class label.
Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment
We highlight one such phenomenon -- temporal distortion -- caused by a misalignment between the rate at which observations of a cascade process are made and the rate at which the process itself operates, and argue that failure to correct for it results in degradation of performance on downstream statistical tasks.
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version
We investigate the power of GCNs, as a function of their number of layers, to distinguish between different random graph models on the basis of the embeddings of their sample graphs.
Fundamental Limits of Deep Graph Convolutional Networks
We give a precise characterization of the set of pairs of graphons that are indistinguishable by a GCN with nonlinear activation functions coming from a certain broad class if its depth is at least logarithmic in the size of the sample graph.
Toward Universal Testing of Dynamic Network Models
Numerous networks in the real world change over time, in the sense that nodes and edges enter and leave the networks.
