Follow our latest advances in machine learning.

Graph machine learning

Generative models

Computer vision

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

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.

We introduce a set of IO-Aware kernels for Graph Neural Networks, bridging the gap between large-scale Graph Machine Learning and hardware-efficient deep learning. The paper was accepted at ICML 2026 as a Spotlight Paper.

On efficient scaling of GNNs via IO-aware layer implementations

Eight papers by the Yandex Research team and our collaborators have been accepted for publication at the International Conference on Machine Learning (ICML 2026).

Papers accepted to ICML 2026

Our paper has been accepted for publication at the Conference on Computer Vision and Pattern Recognition (CVPR 2026).

Paper accepted to CVPR 2026

Six papers by the Yandex Research team and our collaborators have been accepted for publication at the International Conference on Learning Representations (ICLR 2026).

Papers accepted to ICLR 2026

We introduce GraphPFN, a graph foundation model that extends the prior-data fitted network (PFN) framework to graphs, achieving state-of-the-art in-context and finetuned results on real-world graphs.

Blog

Posts

On efficient scaling of GNNs via IO-aware layer implementations

Papers accepted to ICML 2026

Paper accepted to CVPR 2026

Papers accepted to ICLR 2026

GraphPFN: a graph foundation model pretrained on diverse synthetic graphs

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