Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes
Foundational marked temporal point process (MTPP) models, such as the Hawkes process, often use inexpressive model families in order to offer interpretable parameterizations of event data. On the other hand, neural MTPPs…
Point ProcessesRobust variable selection for spatial point processes observed with noise
We propose a method for variable selection in the intensity function of spatial point processes that combines sparsity-promoting estimation with noise-robust model selection. As high-resolution spatial data becomes incre…
Point ProcessesAddressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes
Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the next event. However, existing studies over…
Point ProcessesSpeculative Sampling for Parametric Temporal Point Processes
Temporal point processes are powerful generative models for event sequences that capture complex dependencies in time-series data. They are commonly specified using autoregressive models that learn the distribution of th…
Point ProcessesOn Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
Many scientific fields, from medicine to seismology, rely on analyzing sequences of events over time to understand complex systems. Traditionally, machine learning models must be built and trained from scratch for each n…
Point ProcessesEdit-Based Flow Matching for Temporal Point Processes
Temporal point processes (TPPs) are a fundamental tool for modeling event sequences in continuous time, but most existing approaches rely on autoregressive parameterizations that are limited by their sequential sampling.…
Point ProcessesA Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noise
Ship traffic is an increasing source of underwater radiated noise in coastal waters, motivating real-time digital twins of ocean acoustics for operational noise mitigation. We present a physics-guided probabilistic frame…
Gaussian ProcessesPoint ProcessesIn-Context Learning of Temporal Point Processes with Foundation Inference Models
Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events. Current neural network approaches to…
Point ProcessesRepulsive Monte Carlo on the sphere for the sliced Wasserstein distance
In this paper, we consider the problem of computing the integral of a function on the unit sphere, in any dimension, using Monte Carlo methods. Although the methods we present are general, our guiding thread is the slice…
Point ProcessesDiversified recommendations of cultural activities with personalized determinantal point processes
While optimizing recommendation systems for user engagement is a well-established practice, effectively diversifying recommendations without negatively impacting core business metrics remains a significant industry chall…
Recommendation SystemsPoint ProcessesAre LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
The spread of fake news, polarizing, politically biased, and harmful content on online platforms has been a serious concern. With large language models becoming a promising approach, however, no study has properly benchm…
Point ProcessesPost-training Large Language Models for Diverse High-Quality Responses
Reinforcement learning (RL) has emerged as a popular method for post-training large language models (LLMs). While improving the model's performance on downstream tasks, it often reduces the model's output diversity, lead…
Reinforcement LearningStory GenerationPoint ProcessesSharp Convergence Rates of Empirical Unbalanced Optimal Transport for Spatio-Temporal Point Processes
We statistically analyze empirical plug-in estimators for unbalanced optimal transport (UOT) formalisms, focusing on the Kantorovich-Rubinstein distance, between general intensity measures based on observations from spat…
Point ProcessesLatent Factor Point Processes for Patient Representation in Electronic Health Records
Electronic health records (EHR) contain valuable longitudinal patient-level information, yet most statistical methods reduce the irregular timing of EHR codes into simple counts, thereby discarding rich temporal structur…
Point ProcessesMOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes
Discovering complex causal dependencies in temporal point processes (TPPs) is critical for modeling real-world event sequences. Existing methods typically rely on static or first-order causal structures, overlooking the …
Point ProcessesUnified Flow Matching for Long Horizon Event Forecasting
Modeling long horizon marked event sequences is a fundamental challenge in many real-world applications, including healthcare, finance, and user behavior modeling. Existing neural temporal point process models are typica…
Point ProcessesHT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens
Deep learning has achieved remarkable success in modeling sequential data, including event sequences, temporal point processes, and irregular time series. Recently, transformers have largely replaced recurrent networks i…
Point ProcessesUncovering Causal Relation Shifts in Event Sequences under Out-of-Domain Interventions
Inferring causal relationships between event pairs in a temporal sequence is applicable in many domains such as healthcare, manufacturing, and transportation. Most existing work on causal inference primarily focuses on e…
Causal InferencePoint ProcessesMS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval
Result diversification (RD) is a crucial technique in Text-to-Image Retrieval for enhancing the efficiency of a practical application. Conventional methods focus solely on increasing the diversity metric of image appeara…
DiversityImage RetrievalPoint ProcessesTowards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Deep neural networks have set the state-of-the-art in computer vision tasks such as bounding box detection and semantic segmentation. Object detectors and segmentation models assign confidence scores to predictions, refl…
Objectobject-detectionObject DetectionPoint Processes+1