Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches
Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing history, with numerous models proposed an…
parameter estimationPoint ProcessesDifferentiable Adversarial Attacks for Marked Temporal Point Processes
Marked temporal point processes (MTPPs) have been shown to be extremely effective in modeling continuous time event sequences (CTESs). In this work, we present adversarial attacks designed specifically for MTPP models. A…
Adversarial AttackPoint ProcessesDownlink Performance of Cell-Free Massive MIMO for LEO Satellite Mega-Constellation
Low-earth orbit (LEO) satellite communication (SatCom) has emerged as a promising technology for improving wireless connectivity in global areas. Cell-free massive multiple-input multiple-output (CF-mMIMO), an architectu…
Point ProcessesA Plug-and-Play Bregman ADMM Module for Inferring Event Branches in Temporal Point Processes
An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this s…
Point ProcessesA Point Process Model for Optimizing Repeated Personalized Action Delivery to Users
This paper provides a formalism for an important class of causal inference problems inspired by user-advertiser interaction in online advertiser. Then this formalism is specialized to an extension of temporal marked poin…
Causal InferencePoint ProcessesDeep Linear Hawkes Processes
Marked temporal point processes (MTPPs) are used to model sequences of different types of events with irregular arrival times, with broad applications ranging from healthcare and social networks to finance. We address sh…
Point ProcessesState Space ModelsApplying the maximum entropy principle to neural networks enhances multi-species distribution models
The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluable for understanding species distributio…
Point ProcessesPreventing Conflicting Gradients in Neural Marked Temporal Point Processes
Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex temporal inter-dependencies between labeled events. These models inherently learn two predictive distributions: one for the arrival tim…
Point ProcessesLimit Order Book Event Stream Prediction with Diffusion Model
Limit order book (LOB) is a dynamic, event-driven system that records real-time market demand and supply for a financial asset in a stream flow. Event stream prediction in LOB refers to forecasting both the timing and th…
DenoisingPoint ProcessesPredictionUnsupervised Event Outlier Detection in Continuous Time
Event sequence data record the occurrences of events in continuous time. Event sequence forecasting based on temporal point processes (TPPs) has been extensively studied, but outlier or anomaly detection, especially with…
Anomaly DetectionData AugmentationOutlier DetectionPoint Processes+1A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from over…
ClusteringDiversityPoint ProcessesOptimal Execution under Incomplete Information
We study optimal liquidation strategies under partial information for a single asset within a finite time horizon. We propose a model tailored for high-frequency trading, capturing price formation driven solely by order …
Point ProcessesIntegrating RIS into HAP Networks for Improved Connectivity
This paper investigates a high-altitude platform (HAP) network enhanced with reconfigurable intelligent surfaces (RISs). The arbitrary placement of HAPs and RISs is modeled using stochastic geometry, specifically as homo…
Point ProcessesPoint processes with event time uncertainty
Point processes are widely used statistical models for uncovering the temporal patterns in dependent event data. In many applications, the event time cannot be observed exactly, calling for the incorporation of time unce…
Point ProcessesSmall coresets via negative dependence: DPPs, linear statistics, and concentration
Determinantal point processes (DPPs) are random configurations of points with tunable negative dependence. Because sampling is tractable, DPPs are natural candidates for subsampling tasks, such as minibatch selection or …
Point ProcessesTPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processes
Attention guides our gaze to fixate the proper location of the scene and holds it in that location for the deserved amount of time given current processing demands, before shifting to the next one. As such, gaze deployme…
Point ProcessesUnlocking Point Processes through Point Set Diffusion
Point processes model the distribution of random point sets in mathematical spaces, such as spatial and temporal domains, with applications in fields like seismology, neuroscience, and economics. Existing statistical and…
Point ProcessesMarked Temporal Bayesian Flow Point Processes
Marked event data captures events by recording their continuous-valued occurrence timestamps along with their corresponding discrete-valued types. They have appeared in various real-world scenarios such as social media, …
Point ProcessesRetrieval of Temporal Event Sequences from Textual Descriptions
Retrieving temporal event sequences from textual descriptions is crucial for applications such as analyzing e-commerce behavior, monitoring social media activities, and tracking criminal incidents. To advance this task, …
Point ProcessesRetrieval3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes
A key challenge in automated formal reasoning is the intractable search space, which grows exponentially with the depth of the proof. This branching is caused by the large number of candidate proof tactics which can be a…
Automated Theorem ProvingDiversityPoint Processesvalid