| 2025 | ICML | Measuring Diversity: Axioms and Challenges. | Mikhail Mironov, Liudmila Prokhorenkova |
| 2025 | ICML | Discrete Neural Algorithmic Reasoning. | Gleb Rodionov, Liudmila Prokhorenkova |
| 2023 | ICLR | 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 |
| 2023 | ICLR | Gradient Boosting Performs Gaussian Process Inference. | Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova |
| 2023 | ICML | Which Tricks are Important for Learning to Rank? | Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova |
| 2022 | ICLR | Graph-based Nearest Neighbor Search in Hyperbolic Spaces. | Liudmila Prokhorenkova, Dmitry Baranchuk, Nikolay Bogachev, Yury Demidovich, Alexander Kolpakov |
| 2021 | ICLR | Boost then Convolve: Gradient Boosting Meets Graph Neural Networks. | Sergei Ivanov, Liudmila Prokhorenkova |
| 2021 | ICLR | Uncertainty in Gradient Boosting via Ensembles. | Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko |
| 2021 | ICML | Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures. | Martijn Gsgens, Alexey Tikhonov, Liudmila Prokhorenkova |
| 2021 | ICML | SGLB: Stochastic Gradient Langevin Boosting. | Aleksei Ustimenko, Liudmila Prokhorenkova |
| 2020 | EMNLP | Embedding Words in Non-Vector Space with Unsupervised Graph Learning. | Max Ryabinin, Sergei Popov, Liudmila Prokhorenkova, Elena Voita |
| 2020 | ICML | Graph-based Nearest Neighbor Search: From Practice to Theory. | Liudmila Prokhorenkova, Aleksandr Shekhovtsov |
| 2020 | ICML | StochasticRank: Global Optimization of Scale-Free Discrete Functions. | Aleksei Ustimenko, Liudmila Prokhorenkova |
| 2020 | WAW | Global Graph Curvature. | Liudmila Prokhorenkova, Egor Samosvat, Pim van der Hoorn |
| 2019 | WAW | Using Synthetic Networks for Parameter Tuning in Community Detection. | Liudmila Prokhorenkova |