arXiv AI
Jun 30
On the Faithfulness of Post-Hoc Concept Bottleneck Models
arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.
In Defense of Information Leakage in Concept-based Models
arXiv:2606. 10669v1 Announce Type: cross Abstract: Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.
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arXiv Machine Learning
Jun 16
Assessing Reliability of Symbol Detection in Concept Bottleneck Models
arXiv:2606. 16535v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) are a relevant tool for explainable Artificial Intelligence because they make their predictions through human-interpretable symbols.
arXiv Machine Learning
Jun 8
OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models
arXiv:2606. 07180v1 Announce Type: cross Abstract: The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine learning research.
arXiv Machine Learning
Jun 2
Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs
arXiv:2506. 14003v5 Announce Type: replace Abstract: Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowledge from a trained model, while maintaining its performance on standard tasks.
arXiv Machine Learning
Jun 11
A prior-free blind detection of information leakage from model predictions
arXiv:2606. 11267v1 Announce Type: new Abstract: Data leakage -- contamination of a model with information unavailable at baseline -- is the dominant reproducibility failure in machine-learning-based science, yet detection tools require training code, external data, or domain expertise.
arXiv Machine Learning
Jun 2
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models
arXiv:2503. 07325v2 Announce Type: replace Abstract: Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning.
arXiv Machine Learning
Aug 12
Concept Labels Are Not Enough: Rethinking Concept Bottleneck Models through Representation Integrity
arXiv:2510. 15770v4 Announce Type: replace-cross Abstract: Although deep neural networks achieve strong predictive performance, their internal reasoning often remains difficult to inspect and control.
arXiv AI
Jul 28
When Activation Oracles Learn Not to Read: Concept-Specific Blind Spots in Fine-Tuned Oracles
arXiv:2607. 23379v1 Announce Type: cross Abstract: Activation Oracles (AOs) are language models trained to answer natural-language questions about another model's internal activations.
arXiv AI
Jun 16
Upper Bounds on the Generalization Error of Deep Learning Models via Local Robustness and Stability
arXiv:2606. 16883v1 Announce Type: cross Abstract: Generalization is a critical property of data-driven models, particularly deep learning models deployed in safety-critical applications.
arXiv AI
23h ago
The Model's Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges
arXiv:2608. 17829v1 Announce Type: cross Abstract: LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality.
arXiv Machine Learning
Jun 19
Multimodal Concept Bottleneck Models
arXiv:2606. 19882v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts.
arXiv Machine Learning
Aug 12
Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.