22 citations · 39 across the 6 of their papers we have counts for
7 papers
High-Order Optimization of Gradient Boosted Decision Trees
Jean Pachebat, Sergei Ivanov
Gradient Boosted Decision Trees (GBDTs) are dominant machine learning algorithms for modeling discrete or tabular data. Unlike neural networks with millions of trainable parameters…
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective
Gleb Bazhenov, Sergei Ivanov, Maxim Panov +2
The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore…
High Performance of Gradient Boosting in Binding Affinity Prediction
Dmitrii Gavrilev, Nurlybek Amangeldiuly, Sergei Ivanov +1
Prediction of protein-ligand (PL) binding affinity remains the key to drug discovery. Popular approaches in recent years involve graph neural networks (GNNs), which are used to lea…
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
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…
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
Sergei Ivanov, Liudmila Prokhorenkova
Graph neural networks (GNNs) are powerful models that have been successful in various graph representation learning tasks. Whereas gradient boosted decision trees (GBDT) often outp…
Reinforcement Learning for Combinatorial Optimization: A Survey
Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov +1
Many traditional algorithms for solving combinatorial optimization problems involve using hand-crafted heuristics that sequentially construct a solution. Such heuristics are design…
Understanding Isomorphism Bias in Graph Data Sets
Sergei Ivanov, Sergei Sviridov, Evgeny Burnaev
In recent years there has been a rapid increase in classification methods on graph structured data. Both in graph kernels and graph neural networks, one of the implicit assumptions…