Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
Massively Parallel Exact Inference for Hawkes Processes
Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as $O(N^2)$ in the number of events. The canonical linear exponential Hawkes proces…
Point ProcessesKronecker-Structured Nonparametric Spatiotemporal Point Processes
Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. Classical Poisson and Hawkes processes rely on res…
Point ProcessesData Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point pro…
Data AugmentationPoint ProcessesActive LearningBridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes
Predicting irregularly spaced event sequences with discrete marks poses significant challenges due to the complex, asynchronous dependencies embedded within continuous-time data streams.Existing sequential approaches cap…
Point ProcessesHawkesRank: Event-Driven Centrality for Real-Time Importance Ranking
Quantifying influence in networks is important across science, economics, and public health, yet widely used centrality measures remain limited: they rely on static representations, heuristic network constructions, and p…
Point ProcessesStatistical Consistency of Discrete-to-Continuous Limits of Determinantal Point Processes
We investigate the limiting behavior of discrete determinantal point processes (DPPs) towards continuous DPPs when the size of the set to sample from goes to infinity. We propose a non-asymptotic characterization of this…
Point ProcessesArchetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
Representation learning has been essential for graph machine learning tasks such as link prediction, community detection, and network visualization. Despite recent advances in achieving high performance on these downstre…
Representation LearningCommunity DetectionLink PredictionPoint ProcessesScaling DPPs for RAG: Density Meets Diversity
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding generation in external knowledge, yielding relevance responses that are aligned with factual evidence and evolving corpora. Standard…
Point ProcessesScore-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes
We study sequential change-point detection for spatio-temporal point processes, where actionable detection requires not only identifying when a distributional change occurs but also localizing where it manifests in space…
Change DetectionPoint ProcessesCausal Representation Meets Stochastic Modeling under Generic Geometry
Learning meaningful causal representations from observations has emerged as a crucial task for facilitating machine learning applications and driving scientific discoveries in fields such as climate science, biology, and…
Representation LearningPoint ProcessesAdaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also might get the user out of their comfort zo…
Movie RecommendationPoint ProcessesLong-range Modeling and Processing of Multimodal Event Sequences
Temporal point processes (TPPs) have emerged as powerful tools for modeling asynchronous event sequences. While recent advances have extended TPPs to handle textual information, existing approaches are limited in their a…
Long-range modelingText GenerationPoint ProcessesType predictionReliability-Aware Determinantal Point Processes for Robust Informative Data Selection in Large Language Models
Informative data selection is a key requirement for large language models (LLMs) to minimize the amount of data required for fine-tuning, network distillation, and token pruning, enabling fast and efficient deployment, e…
Point ProcessesTempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation
Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing high-fidelity environments for cybersecurity training. While attack traffic …
Multi-Task LearningIntrusion DetectionPoint ProcessesFrom Hawkes Processes to Attention: Time-Modulated Mechanisms for Event Sequences
Marked Temporal Point Processes (MTPPs) arise naturally in medical, social, commercial, and financial domains. However, existing Transformer-based methods mostly inject temporal information only via positional encodings,…
Time Series ForecastingPoint ProcessesEnhancing Temporal Awareness in LLMs for Temporal Point Processes
Temporal point processes (TPPs) are crucial for analyzing events over time and are widely used in fields such as finance, healthcare, and social systems. These processes are particularly valuable for understanding how ev…
Point ProcessesVOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs
Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that such generated data lacks diversity. In thi…
Point ProcessesScore Matching for Estimating Finite Point Processes
Score matching estimators have garnered significant attention in recent years because they eliminate the need to compute normalizing constants, thereby mitigating the computational challenges associated with maximum like…
Point ProcessesDiverse Video Generation with Determinantal Point Process-Guided Policy Optimization
While recent text-to-video (T2V) diffusion models have achieved impressive quality and prompt alignment, they often produce low-diversity outputs when sampling multiple videos from a single text prompt. We tackle this ch…
Video GenerationPoint ProcessesITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes
Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existing MTPP models rely on channel-mixing st…
Point Processes