We've got several papers accepted to NeurIPS workshops:
- "Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning" by Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov and Dmitry Vetrov has been accepted to the Bayesian Deep Learning Workshop.
- "Low-variance Gradient Estimates for the Plackett-Luce Distribution" by Artyom Gadetsky, Kirill Struminsky, Novi Quadrianto and Dmitry Vetrov in collaboration with Christopher Robinson has been accepted to the Bayesian Deep Learning Workshop.
- "Unsupervised Domain Adaptation with Shared Latent Dynamics for Reinforcement Learning" by Evgenii Nikishin, Arsenii Ashukha and Dmitry Vetrov has also been accepted to the Bayesian Deep Learning Workshop.
- "Structured Sparsification of Gated Recurrent Neural Networks" by Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich and Dmitry Vetrov has been accepted to the workshop on Context and Compositionality in Biological and Artificial Neural Systems.
- Finally, Max Kochurov contributed to the "PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework" paper accepted to the workshop on Program Transformations.