Two-Stage Plans for Estimating the Inverse of a Monotone Function. - Runlong Tang; Moulinath Banerjee; George Michailidis; Shawn Mankad (Technometrics, 2015)

Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models. - Jiahe Lin; Sumanta Basu; Moulinath Banerjee; George Michailidis (J. Mach. Learn. Res., 2016)

Two Simple Ways to Learn Individual Fairness Metrics from Data. - Debarghya Mukherjee; Mikhail Yurochkin; Moulinath Banerjee; Yuekai Sun (ICML, 2020)

Change Point Estimation in a Dynamic Stochastic Block Model. - Monika Bhattacharjee; Moulinath Banerjee; George Michailidis (J. Mach. Learn. Res., 2020)

Inference In High-dimensional Single-Index Models Under Symmetric Designs. - Hamid Eftekhari; Moulinath Banerjee; Yaacov Ritov (J. Mach. Learn. Res., 2021)

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions. - Subha Maity; Yuekai Sun; Moulinath Banerjee (J. Mach. Learn. Res., 2022)

Minimax optimal approaches to the label shift problem in non-parametric settings. - Subha Maity; Yuekai Sun; Moulinath Banerjee (J. Mach. Learn. Res., 2022)

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. - Subha Maity; Debarghya Mukherjee; Moulinath Banerjee; Yuekai Sun (ICLR, 2023)

Understanding new tasks through the lens of training data via exponential tilting. - Subha Maity; Mikhail Yurochkin; Moulinath Banerjee; Yuekai Sun (ICLR, 2023)

Conditional independence testing under misspecified inductive biases. - Felipe Maia Polo; Yuekai Sun; Moulinath Banerjee (NeurIPS, 2023)

Conditional independence testing under model misspecification. - Felipe Maia Polo; Yuekai Sun; Moulinath Banerjee (CoRR, 2023)

Estimating Fréchet bounds for validating programmatic weak supervision. - Felipe Maia Polo; Mikhail Yurochkin; Moulinath Banerjee; Subha Maity; Yuekai Sun (CoRR, 2023)

Weak Supervision Performance Evaluation via Partial Identification. - Felipe Maia Polo; Subha Maity; Mikhail Yurochkin; Moulinath Banerjee; Yuekai Sun (NeurIPS, 2024)

A statistical framework for weak-to-strong generalization. - Seamus Somerstep; Felipe Maia Polo; Moulinath Banerjee; Yaacov Ritov; Mikhail Yurochkin; Yuekai Sun (CoRR, 2024)

Microfoundation inference for strategic prediction. - Daniele Bracale; Subha Maity; Felipe Maia Polo; Seamus Somerstep; Moulinath Banerjee; Yuekai Sun (AISTATS, 2025)

Learning the Distribution Map in Reverse Causal Performative Prediction. - Daniele Bracale; Subha Maity; Yuekai Sun; Moulinath Banerjee (AISTATS, 2025)

A transfer learning framework for weak to strong generalization. - Seamus Somerstep; Felipe Maia Polo; Moulinath Banerjee; Yaacov Ritov; Mikhail Yurochkin; Yuekai Sun (ICLR, 2025)

Dynamic Pricing in the Linear Valuation Model using Shape Constraints. - Daniele Bracale; Moulinath Banerjee; Yuekai Sun; Salam Turki; Kevin Stoll (Trans. Mach. Learn. Res., 2025)

Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policing. - Pramit Das; Moulinath Banerjee; Yuekai Sun (CoRR, 2025)

Optimal Nonlinear Online Learning under Sequential Price Competition via s-Concavity. - Daniele Bracale; Moulinath Banerjee; Cong Shi; Yuekai Sun (CoRR, 2025)

Optimal Intervention for Self-triggering Spatial Networks with Application to Urban Crime Analytics. - Pramit Das; Moulinath Banerjee; Yuekai Sun (CoRR, 2025)