PoPPy simplifies point process modeling and analysis.
problem Efficient modeling and analysis of sequential data.
method Flexible design and efficient learning of point process models.
result PoPPy enables large-scale point process analysis, simulation, and prediction.
The paper models user-advertiser interactions using point processes.
problem Causal inference problems in user-advertiser interaction.
method Temporal marked point processes and neural point processes.
result Neural point processes as practical solutions.
New model distinguishes Poisson processes from self-similar ones.
problem Distinguishing Poisson point processes from self-similar processes.
method Machine learning model based on inhomogeneous, compound Poisson point process.
result The model can distinguish Poisson point processes from self-similar processes.
Paper proposes a new method for point process modeling without intensity function.
problem Limitations of current point process modeling methods, especially in multi-modal distributions.
method Intensity-free approach using Wasserstein distance for likelihood-free learning.
result Superior performance on various synthetic and real-world data compared to conventional methods.
A new model for point processes without intensity function trade-offs.
problem Inefficiency and trade-offs in existing point process models.
method Point Set Diffusion, a diffusion-based latent variable model.
result Achieves state-of-the-art performance in point process generation.
The paper introduces a method to probabilistically select inducing points in sparse Gaussian processes.
problem The challenge is selecting the optimal number of inducing points in sparse Gaussian processes.
method A point process prior is applied to the inducing points, and the posterior is approximated using stochastic variational inference.
result The model learns which and how many inducing points to use, leading to fewer inducing points being preferred as they become less informative.
EFDM models spatial point processes with variable cardinality using existence variables.
problem Challenges in extending diffusion models to variable-cardinality spatial point processes.
method Existence-field diffusion model (EFDM) that jointly models spatial locations and cardinality without discrete transitions.
result EFDM achieves improved modeling capability on datasets with varying cardinality.
Proposes model-based approach for MI learning using point process theory.
problem Lack of statistical point pattern models in MI learning.
method Develops framework using point process theory for principled extensions of MI learning tasks.
result Tractable point pattern models and solutions for MI learning and decision making.
A new method clusters rows of a matrix of point processes.
problem Challenges in analyzing structured point process data.
method Mixture model of multi-level marked point processes, combined with ES algorithm and FPCA.
result An efficient method for clustering rows of a matrix of point processes.
Unified framework detects changes in complex system models.
problem Accurate identification of dynamic changes in simulation models.
method Combines machine learning and process-driven simulation modeling.
result Significantly improves change point detection accuracy.
Extends Hawkes process for flexible residual modeling in point processes.
problem Modeling high-frequency financial data with complex residual distributions.
method Introduces self and mutually exciting point process with discretely Markovian dynamics.
result Flexible residual distributions improve intensity modeling and high-frequency data estimation.
A new model DKMPP integrates covariates and uses an integration-free method for spatio-temporal point processes.
problem Training intractable deep spatio-temporal point processes with multimodal covariates.
method DKMPP uses a deep kernel to model complex relationships and an integration-free score matching method.
result DKMPP and score-based estimators outperform baseline models in spatio-temporal point processes.
Develops methods to answer counterfactual questions in temporal point processes.
problem Lack of counterfactual analysis in temporal point process models.
method Causal model of thinning based on Gumbel-Max structural causal model, superposition theorem, and sampling algorithm.
result Simulation of counterfactual realizations provides valuable insights for targeted interventions.
Paper uses tensor regression to analyze point clouds for process optimization.
problem Challenges in modeling and analyzing high-dimensional point cloud data.
method Utilizes multilinear algebra and tensor regression techniques.
result Successfully models and links point cloud variational patterns to process variables.
Efficiently models multiple correlated point data using generalized LGCPs.
problem Joint modeling of multiple correlated point data.
method Generalized LGCP framework with Gaussian process priors and variational inference.
result Orders of magnitude faster inference compared to existing methods.
New test for point processes without strong model assumptions.
problem Testing local independence in point processes without strong model assumptions.
method Expansion similar to Volterra expansions to represent marginalized intensities.
result Approximation of true marginalized intensity arbitrarily well.
Paper introduces a neural network-based non-stationary influence kernel for complex event data.
problem Modeling complex, non-stationary, and dependent discrete event data.
method Neural Spectral Marked Point Processes (NSMPP) with a versatile non-stationary influence kernel.
result NSMPP outperforms state-of-the-art models on synthetic and real data.
Proposes first privacy-preserving method for estimating Hawkes processes.
problem Estimating point process models with sensitive personal data raises privacy concerns.
method Proposes differential privacy for event stream data and two optimization algorithms.
result Efficiently estimates Hawkes process models with privacy and utility guarantees.
A new model predicts spatio-temporal data using adaptive decision trees and point processes.
problem Predicting spatio-temporal data with real-life applications.
method Hawkes process, adaptive decision tree, joint optimization algorithm.
result Significant improvement in predictions compared to standard methods.
UNIPoint universally approximates point process intensities.
problem How to precisely describe the flexibility of point process models.
method Proof using Stone-Weierstrass Theorem, transfer functions, and recurrent neural networks.
result UNIPoint performs better than other models on synthetic and real-world datasets.
A new model for generating point processes with complex geometries.
problem Difficulties in modeling point processes with large numbers of particles and complex geometries.
method Gradient descent algorithm applied to a phase harmonic operator on wavelet transforms of point patterns.
result The model allows for fast sampling of new configurations that match the statistics of observed point processes.
Proposes a new method to model event sequences using normalizing flows.
problem Modeling asynchronous and probabilistic event sequences.
method Intensity-free framework using normalizing flows.
result Effective at capturing stochasticity of discrete event sequences.
New framework models time-uncertain point processes for better event prediction.
problem Uncertainty in event times in point processes.
method Formulated and discretized continuous-time Hawkes processes with time grid, enabling optimization methods for inference.
result Parameter recovery with O(1/k) convergence rate using gradient descent and VI. Develops a model to learn shared and idiosyncratic patterns in point processes.
problem Learning shared and unique patterns in point processes from diverse observations.
method Developed a parametric point process model with alternating optimization for learning shared structure and idiosyncratic effects.
result The method yields explainable point process models that perform well compared to existing methods.
Proposes a framework for modeling RTB auctions using point processes.
problem Modeling and optimizing repeated auctions in the RTB ecosystem.
method Develops a stochastic framework using point processes to model and optimize RTB auctions.
result The proposed framework can be approximated to a Poisson point process, enabling the use of established properties.
New method learns spatiotemporal dynamics from random point process observations.
problem Challenges in modeling spatiotemporal dynamics from randomly collected data.
method Integration of neural differential equations, neural point processes, implicit neural representations, and amortized variational inference.
result Significant improvements in predictive accuracy and computational efficiency compared to existing methods.
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel …
In this paper we propose the first non-parametric Bayesian model using Gaussian Processes to make inference on Poisson Point Processes without resorting to gridding the domain or to introducing latent thinning points. Unlike competing models that scale cubically and have a squared memory requirement in the number of da…
New method infers and samples point processes from latent diffusion.
problem Modeling point processes with latent diffusion.
method Itô's excursion theory for inference and sampling.
result Proposes a new method to infer and sample point processes.
New MGCPP model for order flow in financial markets.
problem Modeling order flow dynamics in financial markets.
method Developed MGCPP, proved LLN and FCLTs, applied to real data.
result Validated MGCPP model with real trading data.
The paper extends intensity models for limit order books using marked point processes.
problem Modeling intensity ratios in limit order books with state dependency and clustering.
method Developed a new model combining three multiplicative components for marked point processes.
result The new model outperforms other intensity-based methods in predicting market order signs and aggressiveness.
Paper introduces a novel point process model for graph data using GNNs.
problem Modeling discrete event data over graphs with influence kernel.
method Combines Hawkes kernel and Graph Neural Networks (GNN) for event prediction.
result Achieves superior predictive performance compared to state-of-the-art.
EventFlow forecasts event sequences without autoregression, improving accuracy.
problem Forecasting errors in autoregressive models for event sequences.
method EventFlow uses flow matching to learn joint distributions over event times directly.
result EventFlow reduces forecast error by 20%-53% compared to baselines.
Develops variational inference for Neyman-Scott processes for faster sampling.
problem Slow mixing time in MCMC for posterior sampling in Neyman-Scott processes.
method Variational inference algorithm for Neyman-Scott processes, minimizing KL divergence.
result Achieves better prediction performance than MCMC with limited computational time.
Develops a deep non-stationary kernel for non-stationary spatio-temporal point processes.
problem Capturing non-stationary dependencies in point process data.
method Approximates the influence kernel with a novel low-rank decomposition and introduces a log-barrier penalty to maintain non-negativity.
result Demonstrates superior performance and computational efficiency compared to state-of-the-art methods.
This chapter teaches how to model social media events using Hawkes processes.
problem Modeling discrete, inter-dependent events over continuous time in social media.
method Introduction to point processes, Hawkes process, event intensity function, event simulation, parameter estimation.
result Demonstrates modeling retweet cascades using a Hawkes self-exciting process.
New method optimizes Gaussian process allocation for BO.
problem Existing methods for inducing point allocation in BO hinder performance.
method Proposes a new allocation strategy using quality-diversity decomposition.
result Demonstrates improved BO performance through local high-fidelity modeling.
Proposes a flexible neural network model for temporal point processes.
problem Limited expressiveness of RNN-based models for temporal point processes.
method Integrates intensity function using a feedforward neural network and calculates it as its derivative.
result Achieves competitive or superior performance compared to previous methods.
A new model uses neural networks to efficiently learn multivariate temporal point processes.
problem Efficiently modeling multivariate temporal point processes with low parameter complexity.
method Modeling the cumulative hazard function with neural networks for each variate.
result The proposed model achieves state-of-the-art performance on data fitting and event prediction tasks.
A new model for predicting market order book dynamics using a buffer Hawkes process.
problem Predicting the evolution of limit order books in financial markets.
method Introducing a Markovian single point process with a buffer mechanism and self-exciting effect.
result The model accurately predicts market order book dynamics and converges to Brownian motion.
Neural Diffusion Intensity Models simplify Cox processes inference.
problem Intractable nonparametric estimation and posterior inference of latent stochastic intensity in Cox processes.
method Variational framework using neural SDEs, with theoretical guarantee of ELBO maximization coinciding with maximum likelihood estimation.
result Accurate recovery of latent intensity dynamics and posterior paths with significant speedup.
Paper develops a method to predict spatial point processes with guarantees.
problem Predicting the number of events in space with uncertainty.
method Regularized method to learn spatial models with out-of-sample guarantees.
result Method provides valid prediction intervals even when model is misspecified.
This paper tackles efficient learning for factorial marked temporal point processes.
problem Efficient learning for factorial marked temporal point processes.
method Decoupled learning method with two procedures: ADM-M and Fast ISTA, and a reformulated Logistic Regression model.
result Empirical results show the efficiency of the decoupled and reformulated method.
Modeling solar ramping events with spatio-temporal point processes.
problem Predicting solar ramping events influenced by weather conditions.
method Novel spatio-temporal categorical point process model.
result Effective modeling of spatio-temporal correlations in solar ramping events.
Modeling event sequences with RNNs for predictive maintenance.
problem Predicting the intensity function of asynchronous event sequences.
method Use two RNNs: one for background and another for history effects.
result End-to-end training of the model for black-box event intensity prediction.
Survey on modeling event sequences through temporal processes.
problem Modeling phenomena with sequences of events over continuous time.
method Probabilistic models based on point processes, categorized into simple, marked, and spatio-temporal.
result Analysis of existing approaches and their applicability to prediction and modeling.
A new point process for clustering distributions with repulsion.
problem Clustering distributions with repulsion.
method Distributional Determinantal Point Process (dDPP) with sliced Wasserstein kernel.
result Validated dDPP as a well-defined point process and applied to gene expression and epilepsy data.
Deep Random Splines model neural activity data with better dimensionality.
problem Modeling neural population data with shape constraints.
method Deep neural network transforming Gaussian noise into spline parameters.
result Better dimensionality reduction of neural spiking activity.