43 citations · 153 across the 27 of their papers we have counts for
5 papers · 1 filter
Selective Nonparametric Regression via Testing
Fedor Noskov, Alexander Fishkov, Maxim Panov
Prediction with the possibility of abstention (or selective prediction) is an important problem for error-critical machine learning applications. While well-studied in the classifi…
Optimal Noise Reduction in Dense Mixed-Membership Stochastic Block Models under Diverging Spiked Eigenvalues Condition
Fedor Noskov, Maxim Panov
Community detection is one of the most critical problems in modern network science. Its applications can be found in various fields, from protein modeling to social network analysi…
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii +2
Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL…
Scalable Batch Acquisition for Deep Bayesian Active Learning
Aleksandr Rubashevskii, Daria Kotova, Maxim Panov
In deep active learning, it is especially important to choose multiple examples to markup at each step to work efficiently, especially on large datasets. At the same time, existing…
Active Learning for Abstractive Text Summarization
Akim Tsvigun, Ivan Lysenko, Danila Sedashov +9
Construction of human-curated annotated datasets for abstractive text summarization (ATS) is very time-consuming and expensive because creating each instance requires a human annot…