Attribution Inference for Digital Advertising using Inhomogeneous Poisson Models. - Zachary Nichols; Adam Stein (WWW, 2018)

Learning to Select Pivotal Samples for Meta Re-weighting. - Yinjun Wu; Adam Stein; Jacob Gardner; Mayur Naik (AAAI, 2023)

TopEx: Topic-based Explanations for Model Comparison. - Shreya Havaldar; Adam Stein; Eric Wong 001; Lyle H. Ungar (Tiny Papers @ ICLR, 2023)

Faithful Chain-of-Thought Reasoning. - Qing Lyu 001; Shreya Havaldar; Adam Stein; Li Zhang 039; Delip Rao; Eric Wong 001; Marianna Apidianaki; Chris Callison-Burch (IJCNLP (1), 2023)

Rectifying Group Irregularities in Explanations for Distribution Shift. - Adam Stein; Yinjun Wu; Eric Wong 001; Mayur Naik (CoRR, 2023)

MDB: Interactively Querying Datasets and Models. - Aaditya Naik; Adam Stein; Yinjun Wu; Eric Wong 001; Mayur Naik (CoRR, 2023)

Relational Query Synthesis ⋈ Decision Tree Learning. - Aaditya Naik; Aalok Thakkar; Adam Stein; Rajeev Alur; Mayur Naik (Proc. VLDB Endow., 2023)

Towards Compositionality in Concept Learning. - Adam Stein; Aaditya Naik; Yinjun Wu; Mayur Naik; Eric Wong 001 (ICML, 2024)

TorchQL: A Programming Framework for Integrity Constraints in Machine Learning. - Aaditya Naik; Adam Stein; Yinjun Wu; Mayur Naik; Eric Wong 001 (Proc. ACM Program. Lang., 2024)

Towards Style Alignment in Cross-Cultural Translation. - Shreya Havaldar; Adam Stein; Eric Wong 001; Lyle H. Ungar (ACL (1), 2025)

Where's the Bug? Attention Probing for Scalable Fault Localization. - Adam Stein; Arthur Wayne; Aaditya Naik; Mayur Naik; Eric Wong 001 (CoRR, 2025)

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models. - Adam Stein; Aaditya Naik; Neelay Velingker; Mayur Naik; Eric Wong 001 (CoRR, 2025)

Instruction Following by Boosting Attention of Large Language Models. - Vitoria Guardieiro; Adam Stein; Avishree Khare; Eric Wong 001 (CoRR, 2025)