Follow our latest advances in machine learning.

Generative models

Computer vision

Graph machine learning

Distributional shift

Natural language processing 

Nearest neighbor search

Uncertainty estimation 

Large-scale machine learning

Machine learning theory

Neural algorithmic reasoning

Tabular data

Distributed ML

Gradient boosting

Speculative and parallel decoding

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Yandex Research Blog

Three papers by the Yandex Research team (shown in bold below) and our collaborators have been accepted for publication at the International Conference on Learning Representations (ICLR 2025), and one paper has been accepted for publication at the Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025).

Papers accepted to ICLR and NAACL 2025

In 2024, Yandex Research has evolved into several dedicated research groups, each focused on a specific scientific area: Generative Computer Vision, Efficient Large Language Models (LLMs), Graph Neural Networks, and Tabular Deep Learning.

We presented 17 papers at major conferences, including NeurIPS, ICLR, ICML, CVPR, and ACL. Several papers were co-authored with researchers from prominent global institutions, including IST Austria, KAUST Saudi Arabia, Carnegie Mellon University, Meta AI, and Together AI.

Yandex Research in 2024

Our paper has been accepted for an oral presentation at the Learning on Graphs Conference (LoG 2024).

Paper accepted to LoG 2024

Eight papers by the Yandex Research team and our collaborators have been accepted for publication at the Conference on Neural Information Processing Systems (NeurIPS 2024).

Papers accepted to NeurIPS 2024

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Algorithmic reasoning focuses on building models that can execute classic algorithms. It allows one to combine the advantages of neural networks with theoretical guarantees of algorithms.

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.

In this work, we achieve perfect neural execution of several algorithms by forcing the node and edge representations to be from a fixed finite set. Also, the proposed architectural choice allows us to prove the correctness of the learned algorithms for any test data.

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Posts

Papers accepted to ICLR and NAACL 2025

Yandex Research in 2024

Paper accepted to LoG 2024

Papers accepted to NeurIPS 2024

Discrete neural algorithmic reasoning

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