Machine Learning Robustness: A Primer
- URL: http://arxiv.org/abs/2404.00897v3
- Date: Sat, 4 May 2024 00:33:06 GMT
- Title: Machine Learning Robustness: A Primer
- Authors: Houssem Ben Braiek, Foutse Khomh,
- Abstract summary: The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions.
The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines.
The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation.
- Score: 12.426425119438846
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.
Related papers
- Privacy Implications of Explainable AI in Data-Driven Systems [0.0]
Machine learning (ML) models suffer from a lack of interpretability.
The absence of transparency, often referred to as the black box nature of ML models, undermines trust.
XAI techniques address this challenge by providing frameworks and methods to explain the internal decision-making processes.
arXiv Detail & Related papers (2024-06-22T08:51:58Z) - Unified Explanations in Machine Learning Models: A Perturbation Approach [0.0]
Inconsistencies between XAI and modeling techniques can have the undesirable effect of casting doubt upon the efficacy of these explainability approaches.
We propose a systematic, perturbation-based analysis against a popular, model-agnostic method in XAI, SHapley Additive exPlanations (Shap)
We devise algorithms to generate relative feature importance in settings of dynamic inference amongst a suite of popular machine learning and deep learning methods, and metrics that allow us to quantify how well explanations generated under the static case hold.
arXiv Detail & Related papers (2024-05-30T16:04:35Z) - On the Onset of Robust Overfitting in Adversarial Training [66.27055915739331]
Adversarial Training (AT) is a widely-used algorithm for building robust neural networks.
AT suffers from the issue of robust overfitting, the fundamental mechanism of which remains unclear.
arXiv Detail & Related papers (2023-10-01T07:57:03Z) - Doubly Robust Instance-Reweighted Adversarial Training [107.40683655362285]
We propose a novel doubly-robust instance reweighted adversarial framework.
Our importance weights are obtained by optimizing the KL-divergence regularized loss function.
Our proposed approach outperforms related state-of-the-art baseline methods in terms of average robust performance.
arXiv Detail & Related papers (2023-08-01T06:16:18Z) - Topological Interpretability for Deep-Learning [0.30806551485143496]
Deep learning (DL) models cannot quantify the certainty of their predictions.
This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text.
arXiv Detail & Related papers (2023-05-15T13:38:13Z) - On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model,
Data, and Training [109.9218185711916]
Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind social media texts or reviews.
We propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
arXiv Detail & Related papers (2023-04-19T11:07:43Z) - Semantic Image Attack for Visual Model Diagnosis [80.36063332820568]
In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models.
This paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images.
arXiv Detail & Related papers (2023-03-23T03:13:04Z) - A Comprehensive Study on Robustness of Image Classification Models:
Benchmarking and Rethinking [54.89987482509155]
robustness of deep neural networks is usually lacking under adversarial examples, common corruptions, and distribution shifts.
We establish a comprehensive benchmark robustness called textbfARES-Bench on the image classification task.
By designing the training settings accordingly, we achieve the new state-of-the-art adversarial robustness.
arXiv Detail & Related papers (2023-02-28T04:26:20Z) - RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
Federated Learning allows a large number of clients to train a joint model without the need to share their private data.
To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation.
We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
arXiv Detail & Related papers (2021-07-07T15:42:49Z) - Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings [33.25969141014772]
Uncertainty estimation is a widely researched method to highlight the confidence of machine learning systems in deployment.
Sequential and parallel ensemble techniques have shown improved performance of ML systems in multi-modal settings.
We propose an uncertainty-aware boosting technique for multi-modal ensembling in order to focus on the data points with higher associated uncertainty estimates.
arXiv Detail & Related papers (2021-04-21T18:28:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.