Four papers by the Yandex Research team have been accepted for publication at the International Conference on Learning Representations (ICLR 2023).

Papers accepted to ICLR 2023

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

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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We have proposed a benchmark of five diverse heterophilous graphs that come from different domains and exhibit a variety of structural properties. Our benchmark includes a word dependency graph roman-empire, a product co-purchasing network amazon-ratings, a synthetic graph emulating the minesweeper game minesweeper, a crowdsourcing platform worker network tolokers, and a question-answering website interaction network questions.

Follow the cutting-edge research in computer science

Blog

Publications

A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?

A View of Mini-batch SGD via Generating Functions: Conditions of Convergence, Phase Transitions, Benefit from Negative Momenta

Gradient Boosting Performs Gaussian Process Inference

Understanding DDPM Latent Codes Through Optimal Transport