32 citations · 106 across the 9 of their papers we have counts for
10 papers
A Redundancy Reduction Approach for Controllable Sequential Recommendations
Veronika Ivanova, Marina Munkhoeva, Ivan Razvorotnev +1
Sequential recommendation must operate under long-tailed item distributions and popularity-driven concentration, often forcing practitioners to trade short-list accuracy against lo…
Screener: Self-supervised Pathology Segmentation in Medical CT Images
Mikhail Goncharov, Eugenia Soboleva, Mariia Donskova +5
Accurate detection of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes a…
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning
Marina Munkhoeva, Ivan Oseledets
Self-supervised methods received tremendous attention thanks to their seemingly heuristic approach to learning representations that respect the semantics of the data without any ap…
Unsupervised Embedding Quality Evaluation
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches. Despite numerous successes and approaching supervised-level p…
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva +3
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions. Beside…
GRASP: Graph Alignment through Spectral Signatures
Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva +3
What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinfo…
FREDE: Anytime Graph Embeddings
Anton Tsitsulin, Marina Munkhoeva, Davide Mottin +3
Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks. As such embeddings rely, explicitly or impl…
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Graph comparison is a fundamental operation in data mining and information retrieval. Due to the combinatorial nature of graphs, it is hard to balance the expressiveness of the sim…
The Shape of Data: Intrinsic Distance for Data Distributions
Anton Tsitsulin, Marina Munkhoeva, Davide Mottin +4
The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network…
Quadrature-based features for kernel approximation
Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev +1
We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions…