Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
Learning k-Determinantal Point Processes for Personalized Ranking
The key to personalized recommendation is to predict a personalized ranking on a catalog of items by modeling the user's preferences. There are many personalized ranking approaches for item recommendation from implicit f…
DiversityPoint ProcessesRecommendation SystemsHoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?
Accurately forecasting multiple future events within a given time horizon is crucial for finance, retail, social networks, and healthcare applications. Event timing and labels are typically modeled using Marked Temporal …
BenchmarkingPoint ProcessesTime Series ForecastingPareto Front-Diverse Batch Multi-Objective Bayesian Optimization
We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allowed to evaluate a batch of inputs. This …
Bayesian OptimizationDiversityPoint ProcessesNeuro-Symbolic Temporal Point Processes
Our goal is to $\textit{efficiently}$ discover a compact set of temporal logic rules to explain irregular events of interest. We introduce a neural-symbolic rule induction framework within the temporal point process mode…
Point ProcessesOn Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes
Temporal point process (TPP) is an important tool for modeling and predicting irregularly timed events across various domains. Recently, the recurrent neural network (RNN)-based TPPs have shown practical advantages over …
Point ProcessesModeling Randomly Observed Spatiotemporal Dynamical Systems
Spatiotemporal processes are a fundamental tool for modeling dynamics across various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network-based modelin…
Computational EfficiencyPoint ProcessesVariational InferenceComparison of Point Process Learning and its special case Takacs-Fiksel estimation
Recently, Cronie et al. (2024) introduced the notion of cross-validation for point processes and a new statistical methodology called Point Process Learning (PPL). In PPL one splits a point process/pattern into a trainin…
Point ProcessesSystem-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes
Recommender systems are an important part of the modern human experience whose influence ranges from the food we eat to the news we read. Yet, there is still debate as to what extent recommendation platforms are aligned …
Point ProcessesRecommendation SystemsA Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equation
A central problem in quantum mechanics involves solving the Electronic Schrodinger Equation for a molecule or material. The Variational Monte Carlo approach to this problem approximates a particular variational objective…
Point ProcessesVariational Monte CarloNaturally Private Recommendations with Determinantal Point Processes
Often we consider machine learning models or statistical analysis methods which we endeavour to alter, by introducing a randomized mechanism, to make the model conform to a differential privacy constraint. However, certa…
DiversityPoint ProcessesRoTHP: Rotary Position Embedding-based Transformer Hawkes Process
Temporal Point Processes (TPPs), especially Hawkes Process are commonly used for modeling asynchronous event sequences data such as financial transactions and user behaviors in social networks. Due to the strong fitting …
Point ProcessesPositionPredictionFine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
Despite being a key bottleneck in many machine learning tasks, the cost of solving large linear systems has proven challenging to quantify due to problem-dependent quantities such as condition numbers. To tackle this, we…
Point ProcessesPCA for Point Processes
We introduce a novel statistical framework for the analysis of replicated point processes that allows for the study of point pattern variability at a population level. By treating point process realizations as random mea…
Point ProcessesPerformance Analysis for Downlink Transmission in Multi-Connectivity Cellular V2X Networks
With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2…
Point ProcessesA Deep Learning Method for Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks
Merger and Acquisition (M&A) activities play a vital role in market consolidation and restructuring. For acquiring companies, M&A serves as a key investment strategy, with one primary goal being to attain complementariti…
Feature EngineeringPoint ProcessesInterpretable Neural Temporal Point Processes for Modelling Electronic Health Records
Electronic Health Records (EHR) can be represented as temporal sequences that record the events (medical visits) from patients. Neural temporal point process (NTPP) has achieved great success in modeling event sequences …
Point ProcessesTemporal SequencesTransformerLSR: Attentive Joint Model of Longitudinal Data, Survival, and Recurrent Events with Concurrent Latent Structure
In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal …
EpidemiologyPoint ProcessesModeling and Coverage Analysis of K-Tier Integrated Satellite-Terrestrial Downlink Networks
Integrated satellite-terrestrial networks (ISTNs) can significantly expand network coverage while diminishing reliance on terrestrial infrastructure. Despite the enticing potential of ISTNs, there is no comprehensive mat…
Point ProcessesOn the Efficient Marginalization of Probabilistic Sequence Models
Real-world data often exhibits sequential dependence, across diverse domains such as human behavior, medicine, finance, and climate modeling. Probabilistic methods capture the inherent uncertainty associated with predict…
Point ProcessesProbabilistic Generating Circuits -- Demystified
Zhang et al. (ICML 2021, PLMR 139, pp. 12447-1245) introduced probabilistic generating circuits (PGCs) as a probabilistic model to unify probabilistic circuits (PCs) and determinantal point processes (DPPs). At a first g…
Point Processes