Model Interpretation and Explainability towards Creating Transparency in Prediction Models. - Daniel R. Dolk; Donald J. Kridel; Jacob Dineen; David G. Castillo (HICSS, 2020)

Formal Methods for an Iterated Volunteer's Dilemma. - Jacob Dineen; A. S. M. Ahsan-Ul-Haque; Matthew Bielskas (SBP-BRiMS, 2021)

Reinforcement Learning for Data Poisoning on Graph Neural Networks. - Jacob Dineen; A. S. M. Ahsan-Ul-Haque; Matthew Bielskas (SBP-BRiMS, 2021)

Unified Explanations in Machine Learning Models: A Perturbation Approach. - Jacob Dineen; Donald J. Kridel; Daniel R. Dolk; David G. Castillo (HICSS, 2023)

ToW: Thoughts of Words Improve Reasoning in Large Language Models. - Zhikun Xu; Ming Shen; Jacob Dineen; Zhaonan Li; Xiao Ye; Shijie Lu; Aswin RRV; Chitta Baral; Ben Zhou (NAACL (Long Papers), 2025)

QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA. - Jacob Dineen; Aswin RRV; Qin Liu 010; Zhikun Xu; Xiao Ye; Ming Shen; Zhaonan Li; Shijie Lu; Chitta Baral; Muhao Chen 001; Ben Zhou (CoRR, 2025)

BOW: Bottlenecked Next Word Exploration. - Ming Shen; Zhikun Xu; Xiao Ye; Jacob Dineen; Ben Zhou (CoRR, 2025)

CC-LEARN: Cohort-based Consistency Learning. - Xiao Ye; Shaswat Shrivastava; Zhaonan Li; Jacob Dineen; Shijie Lu; Avneet Ahuja; Ming Shen; Zhikun Xu; Ben Zhou (CoRR, 2025)