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2020 – today
- 2025
[j3]Massih-Reza Amini, Vasilii Feofanov, Loïc Pauletto, Lies Hadjadj, Emilie Devijver, Yury Maximov:
Self-training: A survey. Neurocomputing 616: 128904 (2025)- 2024
- [j2]Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data. J. Mach. Learn. Res. 25: 104:1-104:47 (2024) - [c4]Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko:
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias. AISTATS 2024: 595-603 - [c3]Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux, Giuseppe Paolo, Themis Palpanas, Ievgen Redko:
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention. ICML 2024 - [i10]Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko, Jianfeng Zhang, Bo An:
Characterising Gradients for Unsupervised Accuracy Estimation under Distribution Shift. CoRR abs/2401.08909 (2024) - [i9]Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux, Giuseppe Paolo, Themis Palpanas, Ievgen Redko:
Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention. CoRR abs/2402.10198 (2024) - [i8]Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Weijian Deng, Jianfeng Zhang, Bo An:
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts. CoRR abs/2405.18979 (2024) - [i7]Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko:
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. CoRR abs/2406.10327 (2024) - [i6]Vasilii Feofanov, Romain Ilbert, Malik Tiomoko, Themis Palpanas, Ievgen Redko:
User-friendly Foundation Model Adapters for Multivariate Time Series Classification. CoRR abs/2409.12264 (2024) - 2023
- [c2]Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux:
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. ICML 2023: 10008-10033 - [i5]Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux:
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. CoRR abs/2310.13434 (2023) - [i4]Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko:
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias. CoRR abs/2310.14814 (2023) - 2022
- [j1]Vasilii Feofanov
, Emilie Devijver, Massih-Reza Amini:
Wrapper feature selection with partially labeled data. Appl. Intell. 52(11): 12316-12329 (2022) - [i3]Massih-Reza Amini, Vasilii Feofanov, Loïc Pauletto, Emilie Devijver, Yury Maximov:
Self-Training: A Survey. CoRR abs/2202.12040 (2022) - 2021
- [b1]Vasilii Feofanov:
Learning with Partially Labeled Data for Multi-class Classification and Feature Selection. (Classification Multi-classe et Sélection de Variables avec des Données Partiellement Étiquetées). Grenoble Alpes University, France, 2021 - [i2]Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Multi-class Probabilistic Bounds for Self-learning. CoRR abs/2109.14422 (2021)
2010 – 2019
- 2019
- [c1]Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Transductive Bounds for the Multi-Class Majority Vote Classifier. AAAI 2019: 3566-3573 - [i1]Vasilii Feofanov, Massih-Reza Amini, Emilie Devijver:
Semi-supervised Wrapper Feature Selection with Imperfect Labels. CoRR abs/1911.04841 (2019)
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