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 with a focus on stochastic simulations.

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.

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

Compute structure factor of stationary and isotropic point processes

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

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.

Superposition of n i.i.d. one dimensional point processes according to " On the Superposition of Renewal Processes" D. R. Cox and Walter L. Smith