arXiv AI
$\alpha$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
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arXiv Statistics ML
Oct 28
On The Variability of Concept Activation Vectors
The paper investigates how Concept Activation Vectors (CAVs), used for concept‑based explanations in AI, vary due to the randomness in sampling training examples. A theoretical analysis shows that the variance of CAVs decreases roughly as 1/N, where N is the number of sampled examples. Experiments across four modalities confirm this relationship, leading to practical guidelines for efficiently applying CAVs.
arXiv Machine Learning
Aug 24
Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark
arXiv:2602.01718v2 Announce Type: replace Abstract: Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benc...
arXiv Machine Learning
Oct 19
Escaping Parameter Space: Tight Generalization Bounds via Representation Quality
The paper presents a tight generalization bound for deterministic neural networks that relies on the quality of their last hidden layer representations rather than traditional parameter-space metrics. By using a parameter‑free k‑nearest‑neighbor classifier to assess representation quality, the bound decomposes into three measurable components—local class geometry, decoder‑network agreement, and training stability—providing both a non‑vacuous certificate and a diagnostic tool for understanding generalization. Experiments show the bound achieves an 8.0‑percentage‑point gap to test error on CIFAR‑10 with ResNet‑18, yields the first non‑vacuous certificates on CIFAR‑100, and certifies 1.4 % on CIFAR‑10 and 23.1 % on 1,000‑class ImageNet using frozen DINOv2 representations.
arXiv AI
Jun 8
Generalization in Deep Neural Networks: Minimax Rates for Gradient Methods
arXiv:2606. 06772v1 Announce Type: cross Abstract: Understanding the generalization performance of over-parameterized neural networks has become a central topic in deep learning theory.
arXiv AI
Sep 1
ICON Decomposition: Auditing Deep Neural Networks with Multivariate Variance-based Concept-level Explanations
arXiv:2608.26083v2 Announce Type: replace-cross Abstract: Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Auditing for shortcuts requires testing many...
arXiv Machine Learning
Aug 27
ICON Decomposition: Multivariate Concept-Level Explanations of Deep Representations for Model Auditing
ICON Decomposition is a new method for explaining deep neural networks by quantifying how much variance each concept explains in a network layer after accounting for all other concepts and the outcome. Unlike previous concept‑based methods that evaluate concepts in isolation, ICON can distinguish genuine model reliance from spurious correlations. Experiments on synthetic data, skin‑lesion, and brain‑imaging models show that ICON recovers concept importance more accurately, isolates truly relied‑upon concepts, and provides sparse explanations validated through retraining and out‑of‑distribution testing.