δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates. - Dan Ley; Umang Bhatt; Adrian Weller (CoRR, 2021)

Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates. - Dan Ley; Umang Bhatt; Adrian Weller (AAAI, 2022)

Global Counterfactual Explanations: Investigations, Implementations and Improvements. - Dan Ley; Saumitra Mishra; Daniele Magazzeni (CoRR, 2022)

GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations. - Dan Ley; Saumitra Mishra; Daniele Magazzeni (ICML, 2023)

Degraded Polygons Raise Fundamental Questions of Neural Network Perception. - Leonard Tang; Dan Ley (NeurIPS, 2023)

On Minimizing the Impact of Dataset Shifts on Actionable Explanations. - Anna P. Meyer; Dan Ley; Suraj Srinivas; Himabindu Lakkaraju (UAI, 2023)

Consistent Explanations in the Face of Model Indeterminacy via Ensembling. - Dan Ley; Leonard Tang; Matthew Nazari; Hongjin Lin; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Are Large Language Models Post Hoc Explainers? - Nicholas Kroeger; Dan Ley; Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2023)

On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models. - Sree Harsha Tanneru; Dan Ley; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2024)

Generalized Group Data Attribution. - Dan Ley; Suraj Srinivas; Shichang Zhang; Gili Rusak; Himabindu Lakkaraju (CoRR, 2024)