Papers at NeurIPS 2019

We are happy to announce that six papers were accepted for publication at this year's Conference on Neural Information Processing Systems (NeurIPS 2019):

• "Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs" by Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko
• "Minimal Variance Sampling in Stochastic Gradient Boosting" by Bulat Ibragimov and Gleb Gusev
• "Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer" by Arsenii Vanunts and Alexey Drutsa
• "PolyTree framework for tree ensemble analysis" by Igor Kuralenok, Vasilii Ershov, Igor Labutin
• "Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness" by Andrey Malinin and Mark Gales
• "Sequence Modelling with Unconstrained Generation Order" by Dmitrii Emelianenko, Elena Voita, Pavel Serdyukov