Vasilii Feofanov

According to our database, Vasilii Feofanov authored at least 27 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series.

Alexis Roger, Prateek Humane, Zhenghan Tai, Gwen Legate, Andrei Mircea, Vasilii Feofanov, Irina Rish

CoRR, May, 2026

Layer by layer, module by module: Choose both for optimal OOD probing of ViT.

Ambroise Odonnat, Vasilii Feofanov, Laetitia Chapel, Romain Tavenard, Ievgen Redko

CoRR, March, 2026

UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification.

Yessin Moakher, Youssef Attia El Hili, Vasilii Feofanov

CoRR, March, 2026

MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies.

Vasilii Feofanov, Songkang Wen, Jianfeng Zhang, Lujia Pan, Ievgen Redko

CoRR, February, 2026

2025

Leveraging Generic Time Series Foundation Models for EEG Classification.

Théo Gnassounou, Yessin Moakher, Shifeng Xie, Vasilii Feofanov, Ievgen Redko

CoRR, October, 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, August, 2025

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers.

Simon Roschmann, Quentin Bouniot, Vasilii Feofanov, Ievgen Redko, Zeynep Akata

CoRR, June, 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, February, 2025

Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift.

Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko, Jianfeng Zhang, Bo An

Trans. Mach. Learn. Res., 2025

Self-training: A survey.

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

Neurocomputing, 2025

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting.

Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl

Proceedings of the Forty-second International Conference on Machine Learning, 2025

User-Friendly Foundation Model Adapters for Multivariate Time Series Classification.

Romain Ilbert, Vasilii Feofanov, Malik Tiomoko, Ievgen Redko, Themis Palpanas

Proceedings of the 41st IEEE International Conference on Data Engineering, ICDE 2025, 2025

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

Measuring Pre-training Data Quality without Labels for Time Series Foundation Models.

Songkang Wen, Vasilii Feofanov, Jianfeng Zhang

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

Characterising Gradients for Unsupervised Accuracy Estimation under Distribution Shift.

Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko, Jianfeng Zhang, Bo An

CoRR, 2024

MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts.

Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Weijian Deng, Jianfeng Zhang, Bo An

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

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

Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux

Proceedings of the International Conference on Machine Learning, 2023

2022

Self-Training: A Survey.

Massih-Reza Amini, Vasilii Feofanov, Loïc Pauletto, Emilie Devijver, Yury Maximov

CoRR, 2022

Wrapper feature selection with partially labeled data.

Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini

Appl. Intell., 2022

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

PhD thesis, 2021

Multi-class Probabilistic Bounds for Self-learning.

Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini

CoRR, 2021

2019

Semi-supervised Wrapper Feature Selection with Imperfect Labels.

Vasilii Feofanov, Massih-Reza Amini, Emilie Devijver

CoRR, 2019

Transductive Bounds for the Multi-Class Majority Vote Classifier.

Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019