4 research outputs found
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Temporal point process is an expressive tool for modeling event sequences
over time. In this paper, we take a reinforcement learning view whereby the
observed sequences are assumed to be generated from a mixture of latent
policies. The purpose is to cluster the sequences with different temporal
patterns into the underlying policies while learning each of the policy model.
The flexibility of our model lies in: i) all the components are networks
including the policy network for modeling the intensity function of temporal
point process; ii) to handle varying-length event sequences, we resort to
inverse reinforcement learning by decomposing the observed sequence into states
(RNN hidden embedding of history) and actions (time interval to next event) in
order to learn the reward function, thus achieving better performance or
increasing efficiency compared to existing methods using rewards over the
entire sequence such as log-likelihood or Wasserstein distance. We adopt an
expectation-maximization framework with the E-step estimating the cluster
labels for each sequence, and the M-step aiming to learn the respective policy.
Extensive experiments show the efficacy of our method against
state-of-the-arts.Comment: 8 pages, 3 figures, 4 table
Insider Threat Detection via Hierarchical Neural Temporal Point Processes
Insiders usually cause significant losses to organizations and are hard to
detect. Currently, various approaches have been proposed to achieve insider
threat detection based on analyzing the audit data that record information of
the employee's activity type and time. However, the existing approaches usually
focus on modeling the users' activity types but do not consider the activity
time information. In this paper, we propose a hierarchical neural temporal
point process model by combining the temporal point processes and recurrent
neural networks for insider threat detection. Our model is capable of capturing
a general nonlinear dependency over the history of all activities by the
two-level structure that effectively models activity times, activity types,
session durations, and session intervals information. Experimental results on
two datasets demonstrate that our model outperforms the models that only
consider information of the activity types or time alone
Intensity-Free Learning of Temporal Point Processes
Temporal point processes are the dominant paradigm for modeling sequences of
events happening at irregular intervals. The standard way of learning in such
models is by estimating the conditional intensity function. However,
parameterizing the intensity function usually incurs several trade-offs. We
show how to overcome the limitations of intensity-based approaches by directly
modeling the conditional distribution of inter-event times. We draw on the
literature on normalizing flows to design models that are flexible and
efficient. We additionally propose a simple mixture model that matches the
flexibility of flow-based models, but also permits sampling and computing
moments in closed form. The proposed models achieve state-of-the-art
performance in standard prediction tasks and are suitable for novel
applications, such as learning sequence embeddings and imputing missing data.Comment: International Conference on Learning Representations (ICLR) 202
Hawkes Processes Modeling, Inference and Control: An Overview
Hawkes Processes are a type of point process which models self-excitement
among time events. It has been used in a myriad of applications, ranging from
finance and earthquakes to crime rates and social network activity
analysis.Recently, a surge of different tools and algorithms have showed their
way up to top-tier Machine Learning conferences. This work aims to give a broad
view of the recent advances on the Hawkes Processes modeling and inference to a
newcomer to the field.Comment: Fixed typos. Included pseudocodes for simulation algorithms. Improved
figures. Included tables with complexity and performance comparisons.
Included new sections on Current Challenges and Application Example