Jonas Ngnawé

Jonas Ngnawé

PhD Candidate in Computer Science at Université Laval and Mila-Quebec AI Institute

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Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets

Published in NeurIPS 2022 Workshop on Distribution Shifts, 2022

An extension of mixup in the frequency domain that regularizes the deep nets for robustness to common corruptions.

Recommended citation: Ngnawe, J., NJIFON, M. A., Heek, J., and Dauphin, Y. Robustmix: Improving robustness by regularizing the frequency bias of deep nets. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications, 2022. URL https://openreview.net/forum?id=Na64z0YpOx.
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Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers.

Published in NeurIPS 2024, 2024

A novel property of deep robust classifiers that allows to use the logit margin as a proxy score for input margin and efficiently detect non-robust samples, vulnerable to adversarial attacks.

Recommended citation: Ngnawé, J., Sahoo, S., Pequignot, Y., Precioso, F., & Gagné, C. (2024). Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers. The Thirty-eighth Annual Conference on Neural Information Processing Systems.
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A Layer Selection Approach to Test Time Adaptation

Published in AAAI 2025, 2025

A gradient alignement method for layer selection in Test Time Adaptation.

Recommended citation: Sahoo, S., ElAraby, M., Ngnawe, J., Pequignot, Y. B., Precioso, F., & Gagné, C. (2025, April). A layer selection approach to test time adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 19, pp. 20237-20245).
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A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Published in NeurIPS 2025 Reliable ML Workshop, 2025

This work presents the most comprehensive benchmark of robust fine-tuning to date, revealing how architecture, pretraining, and adaptation choices impact robust generalization across diverse datasets, perturbations, and training protocols.

Recommended citation: Heuillet, M., Bhagwatkar, R., Ngnawé. J., Pequignot, Y., Larouche, A., Gagné, C., Rish, I., Ahmad, O., Durand, A. (2025). A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy. arXiv preprint arXiv:2508.14079.
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Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling

Published in ICLR 2026, 2026

We study robust fine-tuning (RFT) of non-robust pretrained models and show that robust objectives cause suboptimal transfer. We propose Epsilon-Scheduling, which enables optimal transfer and improves expected robustness.

Recommended citation: Ngnawé, J., Heuillet, M., Sahoo, S., Pequignot, Y., Ahmad, O., Durand, A., Precioso, F., Gagné, C. (2026). Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling. The Fourteenth International Conference on Learning Representations (ICLR 2026).
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