Liudmila Prokhorenkova
20 papers · 2018–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (7) π Conference Polyglot (4) π Interdisciplinary Bridge π§ Keyword Pioneer π Cross-Pollinator (6)
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Renaissance Researcher
(8)
πΊοΈ
Taxonomy Completionist
(34)
π
Interdisciplinary Bridge
π
Triple Crown
β‘
Prolific Year
(6)
ποΈ
Keyword Collector
(76)
π₯
Unstoppable
(6)
π
Century Club
(20)
β
The Questioner
(2)
Conferences
ICML (7)
NIPS (7)
ICLR (5)
EMNLP (1)
Top co-authors
Keywords
graph neural network
(4)
gradient boosting
(3)
graph algorithm
(2)
learning to rank
(2)
node classification
(2)
bayesian inference
(1)
representation learning
(1)
unsupervised learning
(1)
information retrieval
(1)
self-supervised learning
(1)
graph generation
(1)
graph representation
(1)
graph structure
(1)
out-of-distribution generalization
(1)
stochastic gradient
(1)
nearest neighbor search
(1)
discrete optimization
(1)
uncertainty quantification
(1)
graph embedding
(1)
message passing
(1)
Papers
Measuring Diversity: Axioms and Challenges
ICML 2025
Discrete Neural Algorithmic Reasoning
ICML 2025
Challenges of Generating Structurally Diverse Graphs
NIPS 2024
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
NIPS 2023
Which Tricks are Important for Learning to Rank?
ICML 2023
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
ICLR 2023
Gradient Boosting Performs Gaussian Process Inference
ICLR 2023
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
NIPS 2023
Neural Algorithmic Reasoning Without Intermediate Supervision
NIPS 2023
Graph-based Nearest Neighbor Search in Hyperbolic Spaces
ICLR 2022
Overlapping Spaces for Compact Graph Representations
NIPS 2021
Uncertainty in Gradient Boosting via Ensembles
ICLR 2021
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
ICLR 2021
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures
ICML 2021
SGLB: Stochastic Gradient Langevin Boosting
ICML 2021
Good Classification Measures and How to Find Them
NIPS 2021
Graph-based Nearest Neighbor Search: From Practice to Theory
ICML 2020
StochasticRank: Global Optimization of Scale-Free Discrete Functions
ICML 2020
Embedding Words in Non-Vector Space with Unsupervised Graph Learning
EMNLP 2020
CatBoost: unbiased boosting with categorical features
NIPS 2018