Prokhorenkova, Liudmila

23 publications

ICML 2025 Discrete Neural Algorithmic Reasoning Gleb Rodionov, Liudmila Prokhorenkova

NeurIPS 2025 GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data Gleb Bazhenov, Oleg Platonov, Liudmila Prokhorenkova

ICML 2025 Measuring Diversity: Axioms and Challenges Mikhail Mironov, Liudmila Prokhorenkova

LoG 2025 Revisiting Graph Homophily Measures Mikhail Mironov, Liudmila Prokhorenkova

NeurIPS 2024 Challenges of Generating Structurally Diverse Graphs Fedor Velikonivtsev, Mikhail Mironov, Liudmila Prokhorenkova

TMLR 2024 Evaluating Graph Generative Models with Graph Kernels: What Structural Characteristics Are Captured? Martijn Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova

NeurIPSW 2024 TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features Gleb Bazhenov, Oleg Platonov, Liudmila Prokhorenkova

ICLR 2023 A Critical Look at the Evaluation of GNNs Under Heterophily: Are We Really Making Progress? Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, Liudmila Prokhorenkova

NeurIPS 2023 Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond Oleg Platonov, Denis Kuznedelev, Artem Babenko, Liudmila Prokhorenkova

NeurIPS 2023 Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts Gleb Bazhenov, Denis Kuznedelev, Andrey Malinin, Artem Babenko, Liudmila Prokhorenkova

ICLR 2023 Gradient Boosting Performs Gaussian Process Inference Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova

NeurIPS 2023 Neural Algorithmic Reasoning Without Intermediate Supervision Gleb Rodionov, Liudmila Prokhorenkova

ICML 2023 Which Tricks Are Important for Learning to Rank? Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova

ICLR 2022 Graph-Based Nearest Neighbor Search in Hyperbolic Spaces Liudmila Prokhorenkova, Dmitry Baranchuk, Nikolay Bogachev, Yury Demidovich, Alexander Kolpakov

ICLR 2021 Boost Then Convolve: Gradient Boosting Meets Graph Neural Networks Sergei Ivanov, Liudmila Prokhorenkova

NeurIPS 2021 Good Classification Measures and How to Find Them Martijn Gösgens, Anton Zhiyanov, Aleksey Tikhonov, Liudmila Prokhorenkova

NeurIPS 2021 Overlapping Spaces for Compact Graph Representations Kirill Shevkunov, Liudmila Prokhorenkova

ICML 2021 SGLB: Stochastic Gradient Langevin Boosting Aleksei Ustimenko, Liudmila Prokhorenkova

ICML 2021 Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures Martijn M Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova

ICLR 2021 Uncertainty in Gradient Boosting via Ensembles Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko

ICML 2020 Graph-Based Nearest Neighbor Search: From Practice to Theory Liudmila Prokhorenkova, Aleksandr Shekhovtsov

ICML 2020 StochasticRank: Global Optimization of Scale-Free Discrete Functions Aleksei Ustimenko, Liudmila Prokhorenkova

NeurIPS 2018 CatBoost: Unbiased Boosting with Categorical Features Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin