Prokhorenkova, Liudmila

1. A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
   2023/02/22 by Platonov, Oleg, Kuznedelev, Denis, Diskin, Michael +2 · 45 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG)
2. Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
   2022/09/13 by Oleg Platonov, Denis Kuznedelev, Platonov, Oleg +5 · 8 citations
   Computer Science · #Advanced Graph Neural Networks #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Probability (math.PR) #Recommender Systems and Techniques #Social and Information Networks (cs.SI)
3. Uncertainty in Gradient Boosting via Ensembles
   2020/06/18 by Malinin, Andrey, Prokhorenkova, Liudmila, Ustimenko, Aleksei · 5 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
4. Graph-based Nearest Neighbor Search: From Practice to Theory
   2019/07/01 by Prokhorenkova, Liudmila, Shekhovtsov, Aleksandr · 4 citations
   #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Probability (math.PR)
5. CatBoost: unbiased boosting with categorical features
   2017/06/28 by Prokhorenkova, Liudmila, Gusev, Gleb, Vorobev, Aleksandr +2 · 2 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG)
6. Neural Algorithmic Reasoning Without Intermediate Supervision
   2023/06/23 by Rodionov, Gleb, Prokhorenkova, Liudmila · 2 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG)
7. Discrete Neural Algorithmic Reasoning
   2024/02/18 by Rodionov, Gleb, Prokhorenkova, Liudmila · 2 citations
   #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)
8. Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
   2021/07/15 by Malinin, Andrey, Band, Neil, Ganshin +15 · 1 citation
   #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
9. Which Tricks Are Important for Learning to Rank?
   2022/04/04 by Ivan Lyzhin, Aleksei Ustimenko, Lyzhin, Ivan +5 · 1 citation
   Computer Science · Decision Sciences · #Machine Learning and Algorithms #Domain Adaptation and Few-Shot Learning #Multi-Criteria Decision Making
10. Gradient Boosting Performs Gaussian Process Inference
    2022/06/11 by Aleksei Ustimenko, Ustimenko, Aleksei, Artem Beliakov +3 · 1 citation
    Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
11. Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
    2023/02/27 by Gleb Bazhenov, Denis Kuznedelev, Bazhenov, Gleb +7 · 1 citation
    Computer Science · Decision Sciences · #Advanced Graph Neural Networks #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
12. Revisiting Graph Homophily Measures
    2024/12/12 by Maksim Mironov, Liudmila Prokhorenkova, Mironov, Mikhail +1 · 3 citations
    Computer Science · #Topological and Geometric Data Analysis #Advanced Graph Theory Research #Graph Labeling and Dimension Problems
13. GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
    2024/09/22 by Gleb Bazhenov, Oleg Platonov, Bazhenov, Gleb +3 · 4 citations
    Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Text and Document Classification Technologies
14. Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
    2021/01/21 by Ivanov, Sergei, Prokhorenkova, Liudmila · 1 citation
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Social and Information Networks (cs.SI)
15. Turning Tabular Foundation Models into Graph Foundation Models
    2025/08/28 by Dmitry Eremeev, Eremeev, Dmitry, Gleb Bazhenov +7 · 6 citations
    Computer Science · #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model-Driven Software Engineering Techniques #Semantic Web and Ontologies
16. Measuring Diversity: Axioms and Challenges
    2024/10/18 by Mironov, Mikhail, Prokhorenkova, Liudmila · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG)