conference publication pages are shown as publication year by mistake

[seyedrezamirkhani]

First of all, a big thank you to everyone involved in this project and for providing the service for free.

I noticed that some papers were being shown as being published in the future. For example, Stefan Bauer, Bernhard Schölkopf, Jonas Peters Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2043-2051, 2016. is shown as being published in 2043.

I haven't had a chance to look through this project's code to see how it determines the year a paper is published but
for reference, the BibTex citation for the paper I mentioned above provides the pages and year separately:
@InProceedings{pmlr-v48-bauer16,
title = {The Arrow of Time in Multivariate Time Series},
author = {Bauer, Stefan and Schölkopf, Bernhard and Peters, Jonas},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {2043--2051},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/bauer16.pdf},
url = {https://proceedings.mlr.press/v48/bauer16.html},
abstract = {We prove that a time series satisfying a (linear) multivariate autoregressive moving average (VARMA) model satisfies the same model assumption in the reversed time direction, too, if all innovations are normally distributed. This reversibility breaks down if the innovations are non-Gaussian. This means that under the assumption of a VARMA process with non-Gaussian noise, the arrow of time becomes detectable. Our work thereby provides a theoretic justification of an algorithm that has been used for inferring the direction of video snippets. We present a slightly modified practical algorithm that estimates the time direction for a given sample and prove its consistency. We further investigate how the performance of the algorithm depends on sample size, number of dimensions of the time series and the order of the process. An application to real world data from economics shows that considering multivariate processes instead of univariate processes can be beneficial for estimating the time direction. Our result extends earlier work on univariate time series. It relates to the concept of causal inference, where recent methods exploit non-Gaussianity of the error terms for causal structure learning.}
}