Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Fuente:
arXiv
Saved in:
| Main Authors: | Odonnat, Ambroise, Feofanov, Vasilii, Redko, Ievgen |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
by: Xie, Renchunzi, et al.
Published: (2024)
by: Xie, Renchunzi, et al.
Published: (2024)
Layer by layer, module by module: Choose both for optimal OOD probing of ViT
by: Odonnat, Ambroise, et al.
Published: (2026)
by: Odonnat, Ambroise, et al.
Published: (2026)
Leveraging Generic Time Series Foundation Models for EEG Classification
by: Gnassounou, Théo, et al.
Published: (2025)
by: Gnassounou, Théo, et al.
Published: (2025)
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)
by: Xie, Shifeng, et al.
Published: (2025)
MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
by: Feofanov, Vasilii, et al.
Published: (2026)
by: Feofanov, Vasilii, et al.
Published: (2026)
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
by: Ilbert, Romain, et al.
Published: (2024)
by: Ilbert, Romain, et al.
Published: (2024)
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
by: Ilbert, Romain, et al.
Published: (2024)
by: Ilbert, Romain, et al.
Published: (2024)
Large Language Models as Markov Chains
by: Zekri, Oussama, et al.
Published: (2024)
by: Zekri, Oussama, et al.
Published: (2024)
Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers
by: Roschmann, Simon, et al.
Published: (2025)
by: Roschmann, Simon, et al.
Published: (2025)
Vision Transformer Finetuning Benefits from Non-Smooth Components
by: Odonnat, Ambroise, et al.
Published: (2026)
by: Odonnat, Ambroise, et al.
Published: (2026)
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
by: Feng, Austin, et al.
Published: (2025)
by: Feng, Austin, et al.
Published: (2025)
Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2025)
by: Feofanov, Vasilii, et al.
Published: (2025)
User-friendly Foundation Model Adapters for Multivariate Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2024)
by: Feofanov, Vasilii, et al.
Published: (2024)
Can LLMs predict the convergence of Stochastic Gradient Descent?
by: Zekri, Oussama, et al.
Published: (2024)
by: Zekri, Oussama, et al.
Published: (2024)
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
by: Xie, Renchunzi, et al.
Published: (2024)
by: Xie, Renchunzi, et al.
Published: (2024)
UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification
by: Moakher, Yessin, et al.
Published: (2026)
by: Moakher, Yessin, et al.
Published: (2026)
Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
by: Wen, Songkang, et al.
Published: (2024)
by: Wen, Songkang, et al.
Published: (2024)
Provable Benefits of In-Tool Learning for Large Language Models
by: Houliston, Sam, et al.
Published: (2025)
by: Houliston, Sam, et al.
Published: (2025)
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
by: Lalou, Yanis, et al.
Published: (2024)
by: Lalou, Yanis, et al.
Published: (2024)
LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
by: Roger, Alexis, et al.
Published: (2026)
by: Roger, Alexis, et al.
Published: (2026)
Clustering Head: A Visual Case Study of the Training Dynamics in Transformers
by: Odonnat, Ambroise, et al.
Published: (2024)
by: Odonnat, Ambroise, et al.
Published: (2024)
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
by: Bouniot, Quentin, et al.
Published: (2023)
by: Bouniot, Quentin, et al.
Published: (2023)
Zero-shot Model-based Reinforcement Learning using Large Language Models
by: Benechehab, Abdelhakim, et al.
Published: (2024)
by: Benechehab, Abdelhakim, et al.
Published: (2024)
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
by: Liu, Huafeng, et al.
Published: (2024)
by: Liu, Huafeng, et al.
Published: (2024)
Smart Sampling: Self-Attention and Bootstrapping for Improved Ensembled Q-Learning
by: Khan, Muhammad Junaid, et al.
Published: (2024)
by: Khan, Muhammad Junaid, et al.
Published: (2024)
Provably Robust Pre-Trained Ensembles for Biomarker-Based Cancer Classification
by: Lee, Chongmin, et al.
Published: (2024)
by: Lee, Chongmin, et al.
Published: (2024)
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning
by: Tepeli, Yasin I., et al.
Published: (2024)
by: Tepeli, Yasin I., et al.
Published: (2024)
A Unified Theory of Diversity in Ensemble Learning
by: Wood, Danny, et al.
Published: (2023)
by: Wood, Danny, et al.
Published: (2023)
Diverse Projection Ensembles for Distributional Reinforcement Learning
by: Zanger, Moritz A., et al.
Published: (2023)
by: Zanger, Moritz A., et al.
Published: (2023)
Self-Training: A Survey
by: Amini, Massih-Reza, et al.
Published: (2022)
by: Amini, Massih-Reza, et al.
Published: (2022)
Easing Optimization Paths: a Circuit Perspective
by: Odonnat, Ambroise, et al.
Published: (2025)
by: Odonnat, Ambroise, et al.
Published: (2025)
Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning
by: Radwan, Ahmed, et al.
Published: (2024)
by: Radwan, Ahmed, et al.
Published: (2024)
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
by: Shubham, Kumar, et al.
Published: (2026)
by: Shubham, Kumar, et al.
Published: (2026)
A Sample Efficient Conditional Independence Test in the Presence of Discretization
by: Sun, Boyang, et al.
Published: (2025)
by: Sun, Boyang, et al.
Published: (2025)
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
by: To, Minh Nguyen Nhat, et al.
Published: (2025)
by: To, Minh Nguyen Nhat, et al.
Published: (2025)
Liquid Ensemble Selection for Continual Learning
by: Blair, Carter, et al.
Published: (2024)
by: Blair, Carter, et al.
Published: (2024)
Ensemble Distributionally Robust Bayesian Optimisation
by: Ramazyan, Tigran, et al.
Published: (2026)
by: Ramazyan, Tigran, et al.
Published: (2026)
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
by: Alanova, Shirin, et al.
Published: (2025)
by: Alanova, Shirin, et al.
Published: (2025)
Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments
by: Qu, Yun, et al.
Published: (2025)
by: Qu, Yun, et al.
Published: (2025)
Efficient Ensembles Improve Training Data Attribution
by: Deng, Junwei, et al.
Published: (2024)
by: Deng, Junwei, et al.
Published: (2024)
Similar Items
-
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
by: Xie, Renchunzi, et al.
Published: (2024) -
Layer by layer, module by module: Choose both for optimal OOD probing of ViT
by: Odonnat, Ambroise, et al.
Published: (2026) -
Leveraging Generic Time Series Foundation Models for EEG Classification
by: Gnassounou, Théo, et al.
Published: (2025) -
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025) -
MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
by: Feofanov, Vasilii, et al.
Published: (2026)