Yann Pequignot

LLS: Learnable Layer Selection for Test-Time Adaptation

Suryansh Ram Menon

Frédéric Precioso

While Test-Time Adaptation (TTA) can recover performance drops caused by distribution shifts without requiring source labels, adapting all m… (voir plus)odel parameters frequently leads to catastrophic forgetting and overfitting to noisy test streams. Consequently, layer selection is critical. Existing methods such as GALA (Sahoo 2025) select layers via a cosine alignment proxy between gradient trajectories and parameter updates, a heuristic decoupled from the actual adaptation objective. We propose Learnable Layer Selection (LLS), a differentiable framework that learns continuous per-layer masks. The key insight of this work is not just to learn the layer selection masks, but also the idea to use gradients on one sample to learn the mask on another sample. By evaluating tentative parameter updates on a different, held-out validation sample, we introduce a novel self-supervised generalization loss. This forces the selection mechanism to learn layer masks that generalize to samples not explicitly optimized by current gradients, rather than overfitting. Evaluated on Full DomainBed (ResNet-18, 3 seeds, 16 environments), LLS achieves competitive accuracy across standard TTA algorithms, yielding the best target-split SHOT accuracy of 64.41 ± 0.96%. Systematic evaluations confirm that this cross-sample approach provides a highly stable mechanism for selective parameter adaptation.

2026-09-29

TTCL @ Neural Information Processing Systems (poster)

openreview.net

Efficiency at What Cost? Safety and Fairness in Parameter-Efficient Fine-Tuning of LLMs

Mina Taraghi

Yann Pequignot

Amin Nikanjam

Mohamed Amine Merzouk

Foutse Khomh

2025-12-31

IEEE Transactions on Artificial Intelligence (publié)

doi.org

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling

Ola Ahmad

Frédéric Precioso

Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims… (voir plus) to simultaneously achieve adaptation to a downstream task and robustness to adversarial examples, remains challenging. Despite the abundance of non-robust pretrained models in open-source repositories, their potential for RFT is less understood. We address this knowledge gap by systematically examining RFT from such non-robust models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub _suboptimal transfer_. In challenging scenarios (eg, difficult tasks, high perturbation), the resulting performance can be so low that it may be considered a transfer failure. We find that fine-tuning using a robust objective impedes task adaptation at the beginning of training and eventually prevents optimal transfer. However, we propose a novel heuristic, _Epsilon-Scheduling_, a schedule over perturbation strength used during training that promotes optimal transfer. Additionally, we introduce _expected robustness_, a metric that captures performance across a range of perturbations, providing a more comprehensive evaluation of the accuracy-robustness trade-off of diverse models at test-time. Extensive experiments on wide range of configurations (six pretrained models and five datasets) show that _Epsilon-Scheduling_ successfully prevents _suboptimal transfer_ and consistently improves expected robustness.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

RobustGenBench: A Benchmark for Robust Generalization to Adversarial and Common Perturbations, with Applications to Vision and Vision-Enabled Large Language Models

Ola Ahmad

2025-12-31

Neural Information Processing Systems (Accept (poster))

openreview.net

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

Kaustubh Mani

Yann Pequignot

Vincent Mai

Liam Paull

Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach s… (voir plus)afe exploration through the lens of epistemic uncertainty, where the actor’s sensitivity to parameter perturbations serves as a practical proxy for regions of high uncertainty. We propose Sharpness-Aware Policy Optimization (SHAPO), a sharpness-aware policy update rule that evaluates gradients at perturbed parameters, making policy updates pessimistic with respect to the actor’s epistemic uncertainty. Analytically we show that this adjustment implicitly reweighs policy gradients, amplifying the influence of rare unsafe actions while tempering contributions from already safe ones, thereby biasing learning toward conservative behavior in under-explored regions. Across several continuous-control tasks, our method consistently improves both safety and task performance over existing baselines, significantly expanding their Pareto frontiers.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

openreview.net

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Ola Ahmad

Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations … (voir plus)was pursued by training models from scratch (i.e., with random initializations) using specialized loss objectives. Recently, robust fine-tuning has emerged as a more efficient alternative: instead of training from scratch, pretrained models are adapted to maximize predictive performance and robustness. To conduct robust fine-tuning, practitioners design an optimization strategy that includes the model update protocol (e.g., full or partial) and the specialized loss objective. Additional design choices include the architecture type and size, and the pretrained representation. These design choices affect robust generalization, which is the model's ability to maintain performance when exposed to new and unseen perturbations at test time. Understanding how these design choices influence generalization remains an open question with significant practical implications. In response, we present an empirical study spanning 6 datasets, 40 pretrained architectures, 2 specialized losses, and 3 adaptation protocols, yielding 1,440 training configurations and 7,200 robustness measurements across five perturbation types. To our knowledge, this is the most diverse and comprehensive benchmark of robust fine-tuning to date. While attention-based architectures and robust pretrained representations are increasingly popular, we find that convolutional neural networks pretrained in a supervised manner on large datasets often perform best. Our analysis both confirms and challenges prior design assumptions, highlighting promising research directions and offering practical guidance.

2025-09-28

NeurIPS.cc/2025/Workshop/Reliable_ML (publié)

doi.org

openreview.net

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling

Ola Ahmad

Frédéric Precioso

Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims… (voir plus) to simultaneously achieve adaptation to a downstream task and robustness to adversarial examples, remains challenging. Despite the abundance of non-robust pretrained models in open-source repositories, their potential for RFT is less understood. We address this knowledge gap by systematically examining RFT from such non-robust models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub \emph{suboptimal transfer}. In challenging scenarios (eg, difficult tasks, high perturbation), the resulting performance can be so low that it may be considered a transfer failure. We find that fine-tuning using a robust objective impedes task adaptation at the beginning of training and eventually prevents optimal transfer. However, we propose a novel heuristic, \emph{Epsilon-Scheduling}, a schedule over perturbation strength used during training that promotes optimal transfer. Additionally, we introduce \emph{expected robustness}, a metric that captures performance across a range of perturbations, providing a more comprehensive evaluation of the accuracy-robustness trade-off for diverse models at test time. Extensive experiments on a wide range of configurations (six pretrained models and five datasets) show that \emph{Epsilon-Scheduling} successfully prevents \emph{suboptimal transfer} and consistently improves expected robustness.

2024-12-31

arXiv (prépublication)

doi.org

arxiv.org

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

Frédéric Precioso

Despite extensive research on adversarial training strategies to improve robustness, the decisions of even the most robust deep learning mod… (voir plus)els can still be quite sensitive to imperceptible perturbations, creating serious risks when deploying them for high-stakes real-world applications. While detecting such cases may be critical, evaluating a model's vulnerability at a per-instance level using adversarial attacks is computationally too intensive and unsuitable for real-time deployment scenarios. The input space margin is the exact score to detect non-robust samples and is intractable for deep neural networks. This paper introduces the concept of margin consistency -- a property that links the input space margins and the logit margins in robust models -- for efficient detection of vulnerable samples. First, we establish that margin consistency is a necessary and sufficient condition to use a model's logit margin as a score for identifying non-robust samples. Next, through comprehensive empirical analysis of various robustly trained models on CIFAR10 and CIFAR100 datasets, we show that they indicate high margin consistency with a strong correlation between their input space margins and the logit margins. Then, we show that we can effectively and confidently use the logit margin to detect brittle decisions with such models. Finally, we address cases where the model is not sufficiently margin-consistent by learning a pseudo-margin from the feature representation. Our findings highlight the potential of leveraging deep representations to assess adversarial vulnerability in deployment scenarios efficiently.

2024-12-09

Advances in Neural Information Processing Systems (publié)

doi.org

openreview.net

How to certify machine learning based safety-critical systems? A systematic literature review

Florian Tambon

Gabriel Laberge

Le An

Amin Nikanjam

Paulina Stevia Nouwou Mindom

Yann Pequignot

Foutse Khomh