Point Processes
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Most implemented
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Intensity-Free Learning of Temporal Point Processes
Determinantal point processes for machine learning
Kronecker Determinantal Point Processes
Modelling Behavioural Diversity for Learning in Open-Ended Games
Transformer Hawkes Process
Papers
Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality
We study generative modeling of spatial point processes (SPP), where both the number of points and their spatial configuration are governed by a joint distribution. While diffusion models have achieved strong performance…
Point ProcessesSmooth Neural Point Processes via B-Splines
Temporal point processes (TPPs) provide a general and flexible framework for modeling sequences of events in continuous time. Neural networks have been successfully employed to model TPPs in a highly expressive and data-…
Computational EfficiencyPoint ProcessesFast determinantal sampling on general spaces and diffusion geometry
Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatches, coresets, and other compact representations of large-scale datase…
Point ProcessesFrom Jumps to Signatures: a Generative Method for Temporal Point Processes
Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions. These guarantees do not directly extend to cadlag paths of Temporal Point Processes…
Point ProcessesEfficient Temporal Point Processes via Monotone Alternating Splines
Temporal point processes (TPPs) have widespread applications across various domains. Compared to modeling the conditional intensity of a TPP, modeling its cumulative conditional intensity function (CCIF) improves computa…
Computational EfficiencyPoint ProcessesSeahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expressive intensity models, conditional dens…
Point Processes