Point Processes
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Greatest papers with code
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.
DPPy: Sampling DPPs with Python
Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning.
Zonotope hit-and-run for efficient sampling from projection DPPs
Previous theoretical results yield a fast mixing time of our chain when targeting a distribution that is close to a projection DPP, but not a DPP in general.
Determinantal point processes for machine learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.
PoPPy: A Point Process Toolbox Based on PyTorch
• HongtengXu/PoPPy
• 
In practice, the key points of point process-based sequential data modeling include: 1) How to design intensity functions to describe the mechanism behind observed data?
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
• microsoft/DiCE
• 
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
THAP: A Matlab Toolkit for Learning with Hawkes Processes
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields.
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences.
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
We propose an effective method to solve the event sequence clustering problems based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process.
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
In the V1 dataset, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space, and the noise-correlation.