Do Vision-Language Pretrained Models Learn Primitive Concepts? - Tian Yun 001; Usha Bhalla; Ellie Pavlick; Chen Sun 002 (CoRR, 2022)

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability. - Usha Bhalla; Suraj Srinivas; Himabindu Lakkaraju (NeurIPS, 2023)

Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. - Usha Bhalla; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Do Vision-Language Pretrained Models Learn Composable Primitive Concepts? - Tian Yun 001; Usha Bhalla; Ellie Pavlick; Chen Sun 002 (Trans. Mach. Learn. Res., 2023)

Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). - Usha Bhalla; Alex Oesterling; Suraj Srinivas; Flávio P. Calmon; Himabindu Lakkaraju (NeurIPS, 2024)

Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers. - Alex Oesterling; Usha Bhalla; Suresh Venkatasubramanian; Himabindu Lakkaraju (CoRR, 2024)

All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models. - Charumathi Badrinath; Usha Bhalla; Alex Oesterling; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2024)

Towards Unifying Interpretability and Control: Evaluation via Intervention. - Usha Bhalla; Suraj Srinivas; Asma Ghandeharioun; Himabindu Lakkaraju (CoRR, 2024)

Building Bridges, Not Walls - Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution. - Shichang Zhang; Tessa Han; Usha Bhalla; Hima Lakkaraju (CoRR, 2025)

Interpretability Illusions with Sparse Autoencoders: Evaluating Robustness of Concept Representations. - Aaron J. Li; Suraj Srinivas; Usha Bhalla; Himabindu Lakkaraju (CoRR, 2025)