22 citations · 38 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 22 cited
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…
cs.LG2020
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…
cs.LG2019★ 16 cited
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…