Adil Khan

2 accepted papers

2026

Concepts' Information Bottleneck Models

Karim Galliamov, Syed M Ahsan Kazmi, Adil Khan, Adín Ramírez Rivera

ICLR 2026poster

Concept Bottleneck Models (CBMs) aim to deliver interpretable predictions by routing decisions through a human-understandable concept layer, yet they often suffer reduced accuracy and concept leakage that undermines faithfulness. We introduce an explicit Information Bottleneck regularizer on the con…

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2020

Post-training Iterative Hierarchical Data Augmentation for Deep Networks

Adil Khan, Khadija Fraz

NeurIPS 2020poster

In this paper, we propose a new iterative hierarchical data augmentation (IHDA) method to fine-tune trained deep neural networks to improve their generalization performance. The IHDA is motivated by three key insights: (1) Deep networks (DNs) are good at learning multi-level representations from dat…

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