Two papers have been accepted for publication at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022).

Papers accepted to EMNLP 2022

Five papers have been accepted for publication at the Conference on Neural Information Processing Systems (NeurIPS 2022).

Papers accepted to NeurIPS 2022

Two papers have been accepted for publication at the International Conference on Machine Learning (ICML 2022).

Paper accepted to ICML 2022

At Yandex Research, we advance progress in science and develop technologies for a better tomorrow.

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Join our research team and get an opportunity to engage in world-class applied and theoretical research, gain experience working on high-impact projects and publish at top-tier academic conferences. You will contribute to high-tech services like computer vision technologies, dialogue systems, neural machine translation and self-driving cars. 

Work with us

Yandex Research team regularly contributes to the computer vision research community, mostly in the field of image retrieval and generative modelling.

Computer vision

Language is one of the key forms of communication. We study methods of language representation and understanding to simplify human-computer interactions.

Natural language processing 

Today, training most powerful models often takes significant resources. Our research aims to make large-scale training more efficient and accessible to the entire machine learning community.

Large-scale machine learning

We study various aspects related to theoretical understanding of ML models and algorithms.

Machine learning theory

Graphs are a natural way to represent data from various domains such as social networks, molecules, text, code, etc. We develop and analyze algorithms for graph-structured data.

Graph machine learning

Probabilistic machine learning describes methods which enable reasoning and inference over unknown quantities. Commonly used in generative modelling, regression and uncertainty quantification.

Probabilistic machine learning

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In this post, we share exciting findings from our recent NeurIPS 2022 paper on deep learning and tabular data. We discuss how improved representations of numerical features can turn your vanilla MLPs (multilayer perceptrons) and Transformers into state-of-the-art tabular deep learning solutions that are competitive even with gradient-boosted decision trees, one of the strongest baselines in the world of tabular data problems!

Embeddings for numerical features in tabular deep learning

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Nearest Neighbor Search (NNS) is a long-standing problem arising in many machine learning applications, such as recommender services, information retrieval and others. The goal of the nearest neighbor search is to find the closest element to a given query vector within a database of n elements. Since the sizes of databases in practical tasks are constantly increasing, the scalability of NNS becomes crucial.

Efficient NNS receives much attention from the machine learning community. The problem is well-solved for low-dimensional spaces. However, most practical applications need effective and accurate search in high dimensional ones. Due to the curse of dimensionality, for large d the algorithms for exact NNS provide little improvement over a brute-force algorithm. Thus, both researchers and practitioners are interested in approximate solutions which are efficient and quite accurate.

Recently, methods based on similarity graphs were shown to outperform tree-based and LSH-based techniques according to the public benchmarks. In this post, we discuss graph-based methods from different perspectives. In particular, we cover their scalability, applicability to a wide range of applications, and theoretical properties.

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Blog

Publications

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities

Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity

Optimal Algorithms for Decentralized Stochastic Variational Inequalities

Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees