Bayesian Methods Research Group

1. AAAI 2020

   11 Dec 2019

   Two papers have been accepted to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020):

   • Structured Sparsification of Gated Recurrent Neural Networks by Ekaterina Lobacheva, Nadezhda Chirkova, Aleksandr Markovich, and Dmitry Vetrov
   • Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution by Artyom Gadetsky, Kirill Struminsky, Dmitry Vetrov in collaboration with Christopher Robinson and Novi Quadrianto.

2. NeurIPS 2019 Workshops

   25 Oct 2019

   We've got several papers accepted to NeurIPS workshops:

   1. "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.
   2. "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.
   3. "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.
   4. "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.
   5. Finally, Max Kochurov contributed to the "PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework" paper accepted to the workshop on Program Transformations.

3. NeurIPS 2019

   12 Sep 2019

   This year we've doubled our presence at NeurIPS with four papers accepted:

   1. Importance Weighted Hierarchical Variational Inference by Artem Sobolev and Dmitry Vetrov.
   2. The Implicit Metropolis-Hastings Algorithm by Kirill Neklyudov and Dmitry Vetrov in collaboration with Evgenii Egorov.
   3. A Simple Baseline for Bayesian Uncertainty in Deep Learning by Timur Garipov and Dmitry Vetrov in collaboration with Wesley Maddox, Pavel Izmailov and Andrew Gordon Wilson.
   4. A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models by Maxim Kuznetsov, Daniil Polykovskiy and Dmitry Vetrov in collaboration with Alexander Zhebrak.

   Good academic service is not only about producing novel research, but also about providing critical assessment of other's work. We're proud that Kirill Struminsky, Ekaterina Lobacheva, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov and Dmitry Kropotov were recognized as top 50% reviewers.

4. A paper published in Nature Biotechnology

   02 Sep 2019

   Insilico Medicine published an article in Nature Biotechnology coauthored by our members Maxim Kuznetsov and Daniil Polykovskiy. The paper describes a timed challenge, where the new machine learning system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of DDR1, a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.

5. Deep|Bayes 2019 is over

   29 Aug 2019

   It's this time of a year: once again students from all over the world gathered in Moscow to participate in the Deep|Bayes 2019 – a summer school on Bayesian Deep Learning. Just like the last year, the school featured both lectures and practical assignments. We've been also fortunate to have a couple of invited speakers: Novi Quadrianto from University of Sussex and Higher School of Economics, Maurizio Filippone from EURECOM, Francisco Jesus Rodriguez Ruiz from Columbia University and University of Cambridge, Andrey Malinin from University of Cambridge and Sergey Bartunov from DeepMind.

   For an official press-release see the HSE website. The slides, videos and practicals are available at deepbayes.ru

6. Call for Postdoc on Deep RL

   02 Apr 2019

   We are looking for a postdoc to join our group! Please see details here.

7. Applications to Deep|Bayes 2019 are now open

   16 Feb 2019

   One again, we're organizing an international summer school on Bayesian Deep Learning to be held in Moscow, August 20–25. Head over to deepbayes.ru to view last year's videos, practical assignments and apply to this year's run.

8. ICLR 2019

   21 Dec 2018

   We got 3 papers accepted to ICLR 2019:

   • Variational Autoencoder with Arbitrary Conditioning by Oleg Ivanov, Michael Figurnov and Dmitry Vetrov;
   • Variance Networks: When Expectation Does Not Meet Your Expectations by Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov;
   • The Deep Weight Prior by Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitriy Vetrov in collaboration with Max Welling.

9. NeurIPS 2018 Results

   09 Dec 2018

   This year's NeurIPS conference turned out to be a very fruitful one! We've had

   • Two papers accepted, one of them being a spotlight
     1. Quantifying Learning Guarantees for Convex but Inconsistent Surrogates by Kirill Struminsky and Anton Osokin in collaboration with Simon Lacoste-Julien
     2. Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs by Timur Garipov, Dmitrii Podoprikhin and Dmitry Vetrov in collaboration with Pavel Izmailov and Andrew Gordon Wilson
   • An invited talk by Dmitry Vetrov at the Bayesian Deep Learning workshop
   • Three papers accepted to the aforementioned workshop
     1. Importance Weighted Hierarchical Variational Inference by Artem Sobolev and Dmitry Vetrov
     2. Variational Dropout via Empirical Bayes by Valery Kharitonov, Dmitry Molchanov and Dmitry Vetrov
     3. Subset-Conditioned Generation Using Variational Autoencoder With A Learnable Tensor-Train Induced Prior by Maksim Kuznetsov, Daniil Polykovskiy and Dmitry Vetrov in collaboration with Alexander Zhebrak
   • A paper accepted to the Reinforcement Learning under Partial Observability workshop (contributed talk)
     1. Joint Belief Tracking and Reward Optimization through Approximate Inference by Pavel Shvechikov, Alexander Grishin, Arseny Kuznetsov, Alexander Fritzler and Dmitry Vetrov
   • A paper accepted to the Compact Deep Neural Network Representation with Industrial Applications workshop
     1. Bayesian Sparsification of Gated Recurrent Neural Networks by Ekaterina Lobacheva, Nadezhda Chirkova and Dmitry Vetrov

10. Three papers accepted to ACML 2018

    10 Nov 2018

    We have 3 papers accepted to ACML 2018: Concorde: Morphological Agreement in Conversational Models by Daniil Polykovskiy in collaboration with Dmitry Soloviev and Sergey Nikolenko, ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks by Iurii Kemaev, Daniil Polykovskiy and Dmitry Vetrov, and Extracting Invariant Features From Images Using An Equivariant Autoencoder by Daniil Polykovskiy in collaboration with Denis Kuzminykh, Alexander Zhebrak.

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