no code implementations • 10 Aug 2022 • Imad Aouali, Achraf Ait Sidi Hammou, Sergey Ivanov, Otmane Sakhi, David Rohde, Flavian vasile
We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation.
1 code implementation • 19 May 2022 • Zhiqiang Zhong, Sergey Ivanov, Jun Pang
Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance to GNNs in semi-supervised node classification on graphs with high homophily.
1 code implementation • 19 Jan 2022 • Sergey Ivanov
This textbook covers principles behind main modern deep reinforcement learning algorithms that achieved breakthrough results in many domains from game AI to robotics.
no code implementations • 26 Jul 2021 • Imad Aouali, Sergey Ivanov, Mike Gartrell, David Rohde, Flavian vasile, Victor Zaytsev, Diego Legrand
In this paper, we formulate several Bayesian models that incorporate the reward signal (Reward model), the rank signal (Rank model), or both (Full model), for non-personalized slate recommendation.
no code implementations • 15 Jul 2020 • Max Kochurov, Sergey Ivanov, Eugeny Burnaev
Recently there was an increasing interest in applications of graph neural networks in non-Euclidean geometry; however, are non-Euclidean representations always useful for graph learning tasks?
2 code implementations • 24 Jun 2019 • Sergey Ivanov, Alexander D'yakonov
Recent advances in Reinforcement Learning, grounded on combining classical theoretical results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence tasks and gave birth to Deep Reinforcement Learning (DRL) as a field of research.
1 code implementation • 20 May 2019 • Ivan Lobov, Sergey Ivanov
In this paper we take a problem of unsupervised nodes clustering on graphs and show how recent advances in attention models can be applied successfully in a "hard" regime of the problem.
2 code implementations • ICML 2018 • Sergey Ivanov, Evgeny Burnaev
The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data.
Ranked #17 on Graph Classification on IMDb-B