Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
Enhancing Jailbreak Attacks with Diversity Guidance
As large language models(LLMs) become commonplace in practical applications, the security issues of LLMs have attracted societal concerns. Although extensive efforts have been made to safety alignment, LLMs remain vulner…
DiversityLanguage ModellingPoint ProcessesSafety AlignmentPoint Processes and spatial statistics in time-frequency analysis
A finite-energy signal is represented by a square-integrable, complex-valued function $t\mapsto s(t)$ of a real variable $t$, interpreted as time. Similarly, a noisy signal is represented by a random process. Time-freque…
DenoisingPoint ProcessesAnalyzing Downlink Coverage in Clustered Low Earth Orbit Satellite Constellations: A Stochastic Geometry Approach
Satellite networks are emerging as vital solutions for global connectivity beyond 5G. As companies such as SpaceX, OneWeb, and Amazon are poised to launch a large number of satellites in low Earth orbit, the heightened i…
Point ProcessesBatch Active Learning of Reward Functions from Human Preferences
Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference questions. Active querying methods are commonly …
Active LearningPoint ProcessesExact, Fast and Expressive Poisson Point Processes via Squared Neural Families
We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is fixed and the second layer has a single n…
Gaussian ProcessesPoint ProcessesSelective linear segmentation for detecting relevant parameter changes
Change-point processes are one flexible approach to model long time series. We propose a method to uncover which model parameter truly vary when a change-point is detected. Given a set of breakpoints, we use a penalized …
Model SelectionPoint ProcessesTime SeriesDiversity Measurement and Subset Selection for Instruction Tuning Datasets
We aim to select data subsets for the fine-tuning of large language models to more effectively follow instructions. Prior work has emphasized the importance of diversity in dataset curation but relied on heuristics such …
DiversityInstruction FollowingPoint ProcessesXTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records (EHRs) play a crucial role in patient sa…
Point ProcessesUnveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
In high-stakes systems such as healthcare, it is critical to understand the causal reasons behind unusual events, such as sudden changes in patient's health. Unveiling the causal reasons helps with quick diagnoses and pr…
Point ProcessesCumulative Distribution Function based General Temporal Point Processes
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation …
Information RetrievalPoint ProcessesRecommendation SystemsSelf-Supervised Contrastive Pre-Training for Multivariate Point Processes
Self-supervision is one of the hallmarks of representation learning in the increasingly popular suite of foundation models including large language models such as BERT and GPT-3, but it has not been pursued in the contex…
Point ProcessesRepresentation LearningSelf-Supervised LearningMeta-Learning for Neural Network-based Temporal Point Processes
Human activities generate various event sequences such as taxi trip records, bike-sharing pick-ups, crime occurrence, and infectious disease transmission. The point process is widely used in many applications to predict …
Meta-LearningPoint ProcessesPredictionFast parallel sampling under isoperimetry
We show how to sample in parallel from a distribution $\pi$ over $\mathbb R^d$ that satisfies a log-Sobolev inequality and has a smooth log-density, by parallelizing the Langevin (resp. underdamped Langevin) algorithms. …
Point ProcessesDistribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
Sequences of labeled events observed at irregular intervals in continuous time are ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical framework for modeling these sequences, enabling…
Conformal PredictionPoint ProcessesPredictionUncertainty QuantificationPredicting Future States with Spatial Point Processes in Single Molecule Resolution Spatial Transcriptomics
In this paper, we introduce a pipeline based on XGboost to predict the future distribution of cells that are expressed by the Sog-D gene (active cells) in both the Anterior to posterior (AP) and the Dorsal to Ventral (DV…
Point ProcessesregressionSuper-ResolutionA Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs
Continuously-observed event occurrences, often exhibit self- and mutually-exciting effects, which can be well modeled using temporal point processes. Beyond that, these event dynamics may also change over time, with cert…
Point ProcessesProbabilistic Modeling for Sequences of Sets in Continuous-Time
Neural marked temporal point processes have been a valuable addition to the existing toolbox of statistical parametric models for continuous-time event data. These models are useful for sequences where each event is asso…
Model SelectionPoint ProcessesWeighted least-squares approximation with determinantal point processes and generalized volume sampling
We consider the problem of approximating a function from $L^2$ by an element of a given $m$-dimensional space $V_m$, associated with some feature map $\varphi$, using evaluations of the function at random points $x_1,\do…
Point ProcessesHawkes-based cryptocurrency forecasting via Limit Order Book data
Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency retu…
Point ProcessesTime SeriesErgodic Secrecy Rate Analysis for LEO Satellite Downlink Networks
Satellite networks are recognized as an effective solution to ensure seamless connectivity worldwide, catering to a diverse range of applications. However, the broad coverage and broadcasting nature of satellite networks…
Point Processes