Transductive Bounds for the Multi-Class Majority Vote Classifier. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (AAAI, 2019)

Semi-supervised Wrapper Feature Selection with Imperfect Labels. - Vasilii Feofanov; Massih-Reza Amini; Emilie Devijver (CoRR, 2019)

Multi-class Probabilistic Bounds for Self-learning. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (CoRR, 2021)

Learning with Partially Labeled Data for Multi-class Classification and Feature Selection. (Classification Multi-classe et Sélection de Variables avec des Données Partiellement Étiquetées). - Vasilii Feofanov (2021)

Wrapper feature selection with partially labeled data. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (Appl. Intell., 2022)

Self-Training: A Survey. - Massih-Reza Amini; Vasilii Feofanov; Loïc Pauletto; Emilie Devijver; Yury Maximov (CoRR, 2022)

Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. - Vasilii Feofanov; Malik Tiomoko; Aladin Virmaux (ICML, 2023)

Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias. - Ambroise Odonnat; Vasilii Feofanov; Ievgen Redko (AISTATS, 2024)

SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention. - Romain Ilbert; Ambroise Odonnat; Vasilii Feofanov; Aladin Virmaux; Giuseppe Paolo; Themis Palpanas; Ievgen Redko (ICML, 2024)

Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. - Romain Ilbert; Malik Tiomoko; Cosme Louart; Ambroise Odonnat; Vasilii Feofanov; Themis Palpanas; Ievgen Redko (NeurIPS, 2024)

MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts. - Renchunzi Xie; Ambroise Odonnat; Vasilii Feofanov; Weijian Deng; Jianfeng Zhang; Bo An 001 (NeurIPS, 2024)

Characterising Gradients for Unsupervised Accuracy Estimation under Distribution Shift. - Renchunzi Xie; Ambroise Odonnat; Vasilii Feofanov; Ievgen Redko; Jianfeng Zhang; Bo An 001 (CoRR, 2024)

Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention. - Romain Ilbert; Ambroise Odonnat; Vasilii Feofanov; Aladin Virmaux; Giuseppe Paolo; Themis Palpanas; Ievgen Redko (CoRR, 2024)

Measuring Pre-training Data Quality without Labels for Time Series Foundation Models. - Songkang Wen; Vasilii Feofanov; Jianfeng Zhang (CoRR, 2024)

Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (J. Mach. Learn. Res., 2024)

User-Friendly Foundation Model Adapters for Multivariate Time Series Classification. - Romain Ilbert; Vasilii Feofanov; Malik Tiomoko; Ievgen Redko; Themis Palpanas (ICDEW, 2025)

Self-training: A survey. - Massih-Reza Amini; Vasilii Feofanov; Loïc Pauletto; Lies Hadjadj; Emilie Devijver; Yury Maximov (Neurocomputing, 2025)

Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift. - Renchunzi Xie; Ambroise Odonnat; Vasilii Feofanov; Ievgen Redko; Jianfeng Zhang; Bo An 001 (Trans. Mach. Learn. Res., 2025)

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting. - Abdelhakim Benechehab; Vasilii Feofanov; Giuseppe Paolo; Albert Thomas 001; Maurizio Filippone; Balázs Kégl (CoRR, 2025)

Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification. - Vasilii Feofanov; Songkang Wen; Marius Alonso; Romain Ilbert; Hongbo Guo; Malik Tiomoko; Lujia Pan; Jianfeng Zhang; Ievgen Redko (CoRR, 2025)

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers. - Simon Roschmann; Quentin Bouniot; Vasilii Feofanov; Ievgen Redko; Zeynep Akata (CoRR, 2025)

CauKer: classification time series foundation models can be pretrained on synthetic data only. - Shifeng Xie; Vasilii Feofanov; Marius Alonso; Ambroise Odonnat; Jianfeng Zhang; Themis Palpanas; Ievgen Redko (CoRR, 2025)