Bayesian Methods Research Group

1. 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

2. Call for Postdoc on Deep RL

   02 Apr 2019

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

3. 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.

4. 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.

5. 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

6. 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.

7. A paper is accepted to Molecular Pharmaceutics journal

   19 Sep 2018

   Daniil Polykovskiy and Dmitry Vetrov in collaboration with Insilico Medicine (Alexander Zhebrak, Yan Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine Bozdaganyan, Alexander Aliper, Alex Zhavoronkov, and Artur Kadurin) have published "Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery" paper in the Molecular Pharmaceutics journal where they applied modern Deep Learning techniques to the problem of molecules generation.

8. NIPS 2018: Papers accepted, reviewers appreciated

   08 Sep 2018

   We're happy to inform that our group got two papers accepted to NIPS 2018: "Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs" (spotlight talk) by Timur Garipov*, Dmitrii Podoprikhin*, Dmitry Vetrov in collaboration with Pavel Izmailov* (our alumni) and Andrew Gordon Wilson from Cornell University (* denotes equal contribution) and "Quantifying Learning Guarantees for Convex but Inconsistent Surrogates" by Kirill Struminsky and Anton Osokin (equal contribution) in collaboration with Simon Lacoste-Julien from University of Montreal.

   Also, our reviewers' contribution has been recognized: Anton Osokin made it into top-200 reviewers, and Dmitry Vetrov got in top 30%.

9. First international run of Deep|Bayes, a summer school on Bayesian Deep Learning

   01 Sep 2018

   Over the course of last week over a hundred students from all over the world gathered in Moscow to familiarize themselves with modern research on Bayesian Deep Learning. The school presented an intense in-depth immersion through both lectures and practical assignments. The lecturers included not only the members of our group, but some prominent invited speakers as well: Max Welling from University of Amsterdam, Maurizio Filippone from EURECOM, Alessandro Achille from University of California, Los Angeles, Sergey Bartunov and Michael Figurnov from DeepMind.

   The slides, videos and practicals are available at deepbayes.ru

10. A short paper is accepted to EMNLP 2018

    10 Aug 2018

    A short paper "Bayesian Compression for Natural Language Processing" by Nadezhda Chirkova, Ekaterina Lobacheva and Dmitry Vetrov was accepted to EMNLP 2018, and will be presented in Brussels during the main conference.

11. Professor Vetrov gave a talk at Google DeepMind

    20 Jul 2018

    Dmitry Vetrov was invited to give a talk at Google DeepMind. He presented recent results on Bayesian Deep Learning our group has discovered.

12. BayesGroup has joined forces with Samsung AI Center

    29 May 2018

    Samsung has officially opened its new Samsung AI Center in Moscow, where among others Dmitry Vetrov and our group will be involved in research on Bayesian Methods and Deep Learning.

13. A paper is accepted to ACL 2018

    30 Apr 2018

    A paper "Conditional Generators of Words Definitions" by Artyom Gadetsky, Ilya Yakubovskiy and Dmitry Vetrov was accepted to ACL 2018, and will be presented in Melbourne later this year.

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