Publications

2024

  • MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts

    Renchunzi Xie*, Ambroise Odonnat*, Vasilii Feofanov*, Weijian Deng, Jianfeng Zhang, Bo An
    NeurIPS 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 (Spotlight)

  • Self-Training: A Survey

    Massih-Reza Amini, Vasilii Feofanov, Loic Pauletto, Lies Hadjadj, Emilie Devijver, Yury Maximov
    Neurocomputing

  • 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 (Oral)

  • Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data

    Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
    JMLR + ICML 2024 Presentation

  • Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias

    Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko
    AISTATS 2024
2023

  • Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption

    Vasilii Feofanov*, Malik Tiomoko*, Aladin Virmaux*
    ICML 2023
2022

  • Wrapper Feature Selection with Partially Labeled Data

    Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
    Applied Intelligence
2021

  • Learning with Partially Labeled Data for Multi-class Classification and Feature Selection

    Vasilii Feofanov
    PhD Thesis
2019

  • Transductive Bounds for the Multi-class Majority Vote Classifier

    Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
    AAAI 2019 (Oral)