On Optimizing Human-Machine Task Assignments. - Andreas Veit; Michael J. Wilber; Rajan Vaish; Serge J. Belongie; James Davis 001; Vishal Anand 002; Anshu Aviral; Prithvijit Chakrabarty; Yash Chandak; Sidharth Chaturvedi; Chinmaya Devaraj; Ankit Dhall; et al. (CoRR, 2015)

Learning to Generate Natural Language Rationales for Game Playing Agents. - Upol Ehsan; Pradyumna Tambwekar; Larry Chan; Brent Harrison; Mark O. Riedl (AIIDE Workshops, 2018)

Controllable Neural Story Generation via Reinforcement Learning. - Pradyumna Tambwekar; Murtaza Dhuliawala; Animesh Mehta; Lara J. Martin; Brent Harrison; Mark O. Riedl (CoRR, 2018)

Controllable Neural Story Plot Generation via Reward Shaping. - Pradyumna Tambwekar; Murtaza Dhuliawala; Lara J. Martin; Animesh Mehta; Brent Harrison; Mark O. Riedl (IJCAI, 2019)

Automated rationale generation: a technique for explainable AI and its effects on human perceptions. - Upol Ehsan; Pradyumna Tambwekar; Larry Chan; Brent Harrison; Mark O. Riedl (IUI, 2019)

Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers. - Andrew Silva; Pradyumna Tambwekar; Matthew C. Gombolay (NAACL-HLT, 2021)

Interpretable Policy Specification and Synthesis through Natural Language and RL. - Pradyumna Tambwekar; Andrew Silva; Nakul Gopalan; Matthew C. Gombolay (CoRR, 2021)

Commander's Intent: A Dataset and Modeling Approach for Human-AI Task Specification in Strategic Play. - Pradyumna Tambwekar; Nathan Vaska; Lakshita Dodeja; Matthew C. Gombolay (CoRR, 2022)

FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings. - Andrew Silva; Pradyumna Tambwekar; Matthew C. Gombolay (CoRR, 2022)

FedPerC: Federated Learning for Language Generation with Personal and Context Preference Embeddings. - Andrew Silva; Pradyumna Tambwekar; Matthew C. Gombolay (EACL (Findings), 2023)

A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting. - Pradyumna Tambwekar; Lakshita Dodeja; Nathan Vaska; Wei Xu; Matthew C. Gombolay (EMNLP (Findings), 2023)

Towards Reconciling Usability and Usefulness of Explainable AI Methodologies. - Pradyumna Tambwekar; Matthew C. Gombolay (CoRR, 2023)

Natural Language Specification of Reinforcement Learning Policies Through Differentiable Decision Trees. - Pradyumna Tambwekar; Andrew Silva; Nakul Gopalan; Matthew C. Gombolay (IEEE Robotics Autom. Lett., 2023)

Towards Balancing Preference and Performance through Adaptive Personalized Explainability. - Andrew Silva; Pradyumna Tambwekar; Mariah Schrum; Matthew C. Gombolay (HRI, 2024)

Towards reconciling usability and usefulness of policy explanations for sequential decision-making systems. - Pradyumna Tambwekar; Matthew C. Gombolay (Frontiers Robotics AI, 2024)

Interactive and Explainable Robot Learning: A Comprehensive Review. - Esmaeil Seraj; Kin Man Lee; Zulfiqar Zaidi; Qingyu Xiao; Zhaoxin Li; Arthur Nascimento; Sanne van Waveren; Pradyumna Tambwekar; Rohan R. Paleja; Devleena Das; Matthew C. Gombolay (Found. Trends Robotics, 2024)

Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks. - Lakshita Dodeja; Pradyumna Tambwekar; Erin Hedlund-Botti; Matthew C. Gombolay (Int. J. Hum. Comput. Stud., 2024)

Generating CAD Code with Vision-Language Models for 3D Designs. - Kamel Alrashedy; Pradyumna Tambwekar; Zulfiqar Haider Zaidi; Megan Langwasser; Wei Xu; Matthew C. Gombolay (ICLR, 2025)