Discovering Topical Interactions in Text-based Cascades using Hidden Markov Hawkes Processes

从实例级自注意力 Hawkes 过程中学习 Granger 因果关系

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

提出 ISAHP 神经点过程框架，利用 transformer 自注意力直接从多类型异步事件序列中无监督推断实例级 Granger 因果关系，并在因果发现与类型预测任务上达到最优性能。

The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM. This generative model allows past events to influence the future in complex …

具有观测间隙的多元Hawkes过程

A Multivariate Hawkes Process with Gaps in Observations

本文研究了在观测存在间隙的情况下，利用多元Hawkes过程学习隐藏的定向关系。通过引入边界条件，解决了缺失事件的处理问题，并展示了在稀疏观测下MHPG仍能实现稳健恢复。

The statistical physics of discovering exogenous and endogenous factors in a chain of events

Event occurrence is not only subject to the environmental changes, but is also facilitated by the events that have occurred in a system. Here, we develop a method for estimating such extrinsic and intrinsic factors from a single series of event-occurrence times. The analysis is performed using a model that combines the inhomogeneous Poisson process and the Hawkes process, which represent exogenous fluctuations and endogenous chain-reaction mechanisms, respectively. The model is fit to a given dataset by minimizing the free energy, for which statistical physics and a path-integral method are ut…

Sparse Topical Coding

We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function. Such relaxations make STC amenable to: 1) directly control the sparsity of inferred representations by using sparsity-inducing regularizers; 2) be seamlessly integrated with a convex error function (e.g., SVM hinge loss) for supervised learning; and 3) be efficiently learned w…

Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes

Sequences of events including infectious disease outbreaks, social network activities, and crimes are ubiquitous and the data on such events carry essential information about the underlying diffusion processes between communities (e.g., regions, online user groups). Modeling diffusion processes and predicting future events are crucial in many applications including epidemic control, viral marketing, and predictive policing. Hawkes processes offer a central tool for modeling the diffusion processes, in which the influence from the past events is described by the triggering kernel. However, the …