Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Temporal Point Processes via Reinforcement Learning   Social goods, such as healthcare, smart city, and information networks, often
produce ordered event data in continuous time. The generative processes of
these event data can be very complex, requiring flexible models to capture
their dynamics. Temporal point processes offer an elegant framework for
modeling event data without discretizing the time. However, the existing
maximum-likelihood-estimation (MLE) learning paradigm requires hand-crafting
the intensity function beforehand and cannot directly monitor the
goodness-of-fit of the estimated model in the process of training. To alleviate
the risk of model-misspecification in MLE, we propose to generate samples from
the generative model and monitor the quality of the samples in the process of
training until the samples and the real data are indistinguishable. We take
inspiration from reinforcement learning (RL) and treat the generation of each
event as the action taken by a stochastic policy. We parameterize the policy as
a flexible recurrent neural network and gradually improve the policy to mimic
the observed event distribution. Since the reward function is unknown in this
setting, we uncover an analytic and nonparametric form of the reward function
using an inverse reinforcement learning formulation. This new RL framework
allows us to derive an efficient policy gradient algorithm for learning
flexible point process models, and we show that it performs well in both
synthetic and real data.

9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas