Code and real data for "Enhancing Human Learning via Spaced Repetition Optimization", PNAS 2019

Code and data for "Deep Reinforcement Learning of Marked Temporal Point Processes", NeurIPS 2018

A diffusion-based framework for spatio-temporal point processes

Pieces of code that have appeared on my blog.

Code for "Hierarchical Dirichlet-Hawkes process: generative model and inference algorithm", WWW 2017

Code for "An Online Algorithm to Reduce the Spread of Misinformation in Social Networks", WSDM 2018

An implementation of a differentiable point process and a differentiable spiking neural network.

Compute structure factor of stationary and isotropic point processes

Code and data for "Stochastic Optimal Control of Epidemic Processes in Networks", ML4H at NeurIPS 2018

A novel general non-stationary point process model based on neural networks.

Sub-package of spatstat containing code for linear networks

Sub-package of spatstat containing functions for random generation

A LSTM based adversarial learning framework for anomaly detection.

Code for "Survival Permanental Processes for Survival Analysis with Time-Varying Covariates" at NeurIPS2023

This document contains Henry's study notes.

Some implementations of partial rejection samplers

Codebase for generating visuals with dynamical systems over cyclic rings.

Temporal point process models for time-limited coupon prediction (KDD 2017).

The NHMC-AR model is a Non-Homogeneous Markov Chain AutoRegressive model. It is designed to perform context-sensitive forecasting in time series that are associated with event sequences.