Exact Procrustes matching of n Gaussian vectors in d≥polylog(n) dimensions is achievable in polynomial time whenever the correlation satisfies ρ²>√α≈0.58, via counting wide trees.
Distributed optimization and statistical learning via the alternating direction method of multipliers , Volume =
3 Pith papers cite this work. Polarity classification is still indexing.
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ARCH is a hierarchical flow-based generative model that enables tractable conditional intensity computation and arbitrary conditioning for spatiotemporal event distributions.
SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.
citing papers explorer
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High-Dimensional Procrustes Matching via Tree Counts
Exact Procrustes matching of n Gaussian vectors in d≥polylog(n) dimensions is achievable in polynomial time whenever the correlation satisfies ρ²>√α≈0.58, via counting wide trees.
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Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
ARCH is a hierarchical flow-based generative model that enables tractable conditional intensity computation and arbitrary conditioning for spatiotemporal event distributions.
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Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.