Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (NeurIPS, 2022)

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. - Satyapriya Krishna; Tessa Han; Alex Gu; Javin Pombra; Shahin Jabbari; Steven Wu 001; Himabindu Lakkaraju (CoRR, 2022)

Efficient Estimation of the Local Robustness of Machine Learning Models. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Is Ignorance Bliss? The Role of Post Hoc Explanation Faithfulness and Alignment in Model Trust in Laypeople and Domain Experts. - Tessa Han; Yasha Ektefaie; Maha R. Farhat; Marinka Zitnik; Himabindu Lakkaraju (CoRR, 2023)

Hevelius Report: Visualizing Web-Based Mobility Test Data For Clinical Decision and Learning Support. - Hongjin Lin; Tessa Han; Krzysztof Z. Gajos; Anoopum S. Gupta (ASSETS, 2024)

MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models. - Tessa Han; Aounon Kumar; Chirag Agarwal; Himabindu Lakkaraju (NeurIPS, 2024)

Characterizing Data Point Vulnerability as Average-Case Robustness. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (UAI, 2024)

Towards Safe and Aligned Large Language Models for Medicine. - Tessa Han; Aounon Kumar; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2024)

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. - Satyapriya Krishna; Tessa Han; Alex Gu; Steven Wu 001; Shahin Jabbari; Himabindu Lakkaraju (Trans. Mach. Learn. Res., 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)