Venue: NeurIPS 2020 Workshop DL-IG

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This is a wonderful approach called the Additive Poisson Process for estimating the intensity of a multi-dimensional Poisson process using a lower-dimensional projections in a Generalized Additive Model (GAM). The GAM is then connected to a log-linear model. The paper exploits the information geometric structure of the log-linear model to fit the intensity using natural gradient descent. Experimental results on four-dimensional simulated data are provided.

This is an innovative approach, to my knowledge, for estimating the joint intensity. It would be good to motivate this work using a specific application, say for problems in forestry.
review_title: Review

final_score: rating: 7: Good paper, accept
review_content: This paper considers the challenging problem of modeling multivariate interactions in point processes. The approach uses methods from information geometry that decompose distributions in terms of a partial ordering over higher-order interactions and applies this logic to the intensity function of a point process. Small synthetic experiments demonstrate the feasibility of recovering a model of higher-order interactions from data, with some expected caveats that over-fitting can happen more easily. 

This seems an interesting and challenging problem that is relevant for the workshop. For future work, it would be nice to develop some intuition about real-world situations where higher-order point process interactions could arise, and demonstrate relevance. It seems that the point data was ultimately discretized, in which case it wasn't clear how this approach is specific to temporal data. Perhaps it has to do with the autoregressive structure of the intensity function, but the formulation and notation in Eq. 5 was not very clear to me.
review_title: Review for "Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds"

Decision: Accept (Poster)