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Regular version of the site

Samsung-HSE Laboratory

Publications
Article
Randomized Block Cubic Newton Method In press

Doikov Nikita, Richtarik P.

Proceedings of Machine Learning Research. 2018. No. 80. P. 1290-1298.

Book chapter
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates In press

Struminsky K., Lacoste-Julien S., Osokin A.

In bk.: Advances in Neural Information Processing Systems 31 (NIPS 2018). 2018.

Samsung-HSE Laboratory is a new research lab of the Faculty of Computer Science. The main direction of the Laboratory’s research is the construction of scalable probabilistic models. The core of the new Laboratory is a team of researchers of the Centre of Deep Learning and Bayesian Methods, with a broad expertise in the field of machine learning and Bayesian methods.

Samsung, which is one of the world's technological leaders, creates a network of joint laboratories around the world. The participation of HSE’s staff in this global project will allow them to focus on fundamental research and contact with the world's strongest research groups in the field of machine learning and artificial intelligence.

The major areas of research are:

  • Sparsification and acceleration of deep neural networks
  • Ensembles of ML algorithms
  • Uncertainty estimation and defences against adversarial attacks
  • Loss-based learning for Deep Structured Prediction
  • Stochastic optimization methods
  • Learning and inference methods for probabilistic models using tensor decomposition