Ambroise Odonnat

According to our database1, Ambroise Odonnat authored at least 18 papers between 2024 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Layer by layer, module by module: Choose both for optimal OOD probing of ViT.
CoRR, March, 2026

Vision Transformer Finetuning Benefits from Non-Smooth Components.
CoRR, February, 2026

2025
Optimal Self-Consistency for Efficient Reasoning with Large Language Models.
CoRR, November, 2025

Provable Benefits of In-Tool Learning for Large Language Models.
CoRR, August, 2025

CauKer: classification time series foundation models can be pretrained on synthetic data only.
CoRR, August, 2025

Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift.
Trans. Mach. Learn. Res., 2025

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities.
Trans. Mach. Learn. Res., 2025

Zero-shot Model-based Reinforcement Learning using Large Language Models.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Easing Optimization Paths: a Circuit Perspective.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

2024
A Visual Case Study of the Training Dynamics in Neural Networks.
CoRR, 2024

Large Language Models as Markov Chains.
CoRR, 2024

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation.
CoRR, 2024

Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention.
CoRR, 2024

Characterising Gradients for Unsupervised Accuracy Estimation under Distribution Shift.
CoRR, 2024

MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts.
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.
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.
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.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024


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