The list below includes published and accepted papers, current preprints, and manuscripts under review. Publications coauthored with supervised trainees are marked accordingly.

2026+

  1. Doubly Robust Estimation and Inference for Heterogeneous Treatment Effects with Interval-Censored Outcomes

    Y. Xu and S. Maity

    Submitted to the Journal of the American Statistical Association: Theory and Methods

  2. The Statistical Cost of Adaptation in Multi-Source Transfer Learning

    A. Chakraborty and S. Maity

    Submitted to JMLR Preprint

  3. Robust inference for risk heterogeneity under group imbalance

    M. Xu, S. Maity, and J. Dubin

    To be submitted to Statistics in Biosciences Preprint Trainee coauthor

  4. Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach

    M. Cheng, S. Maity, Q. Tian, and P. Li

    Scandinavian Journal of Statistics, accepted Preprint Trainee coauthor

  5. CARROT: A Cost Aware Rate Optimal Router

    S. Somerstep, F. Maia Polo, A. F. M. de Oliveira, P. Mangal, M. Silva, O. Bhardwaj, M. Yurochkin, and S. Maity

    Annals of Applied Statistics, accepted Preprint

2025

  1. How does overparametrization affect performance on minority groups?

    S. Roy, S. Maity, S. Xue, M. Yurochkin, and Y. Sun

    Transactions on Machine Learning Research Paper

  2. Diagnosis-based mortality prediction for intensive care unit patients via transfer learning

    M. Xu, S. Maity, and J. Dubin

    Under review at the Canadian Journal of Statistics Preprint Trainee coauthor

  3. Learning the distribution map in reverse causal performative prediction

    D. Bracale, S. Maity, Y. Sun, and M. Banerjee

    AISTATS 2025; Proceedings of Machine Learning Research 258, 973–981 Paper

  4. Limitations of refinement methods for weak-to-strong generalization

    S. Somerstep, Y. A. Ritov, M. Yurochkin, S. Maity, and Y. Sun

    COLM 2025 Paper

  5. Microfoundation inference for strategic prediction

    D. Bracale, F. Maia Polo, S. Maity, S. Somerstep, M. Banerjee, and Y. Sun

    AISTATS 2025; Proceedings of Machine Learning Research 258, 919–927 Paper

2024

  1. A linear adjustment-based approach to posterior drift in transfer learning

    S. Maity, D. Dutta, J. Terhorst, Y. Sun, and M. Banerjee

    Biometrika 111(1), 31–50 Paper

  2. Weak supervision performance evaluation via partial identification

    F. Maia Polo, S. Maity, M. Yurochkin, M. Banerjee, and Y. Sun

    NeurIPS 2024 Paper

  3. An investigation of representation and allocation harms in contrastive learning

    S. Maity, M. Agarwal, M. Yurochkin, and Y. Sun

    ICLR 2024 Paper

  4. Aligners: Decoupling LLMs and alignment

    L. Ngweta, M. Agarwal, S. Maity, A. Gittens, Y. Sun, and M. Yurochkin

    Findings of EMNLP 2024 Paper

2023

  1. Simple disentanglement of style and content in visual representations

    L. Ngweta, S. Maity, A. Gittens, Y. Sun, and M. Yurochkin

    ICML 2023 Paper

  2. Bayes classifier cannot be learned from noisy responses with unknown noise rates

    S. Bakshi and S. Maity

    ICLR Tiny Papers 2023 Paper

  3. Understanding new tasks through the lens of training data via exponential tilting

    S. Maity, M. Yurochkin, M. Banerjee, and Y. Sun

    ICLR 2023 Paper

  4. Predictor-corrector algorithms for stochastic optimization under gradual distribution shift

    S. Maity, D. Mukherjee, M. Banerjee, and Y. Sun

    ICLR 2023 Paper

2022

  1. Minimax optimal approaches to the label shift problem in non-parametric settings

    S. Maity, Y. Sun, and M. Banerjee

    Journal of Machine Learning Research 23(346), 1–45 Paper

  2. Meta-analysis of heterogeneous data: Integrative sparse regression in high dimensions

    S. Maity, Y. Sun, and M. Banerjee

    Journal of Machine Learning Research 23(198), 1–50 Paper

  3. Role of multiresolution vulnerability indices in COVID-19 spread in India: A Bayesian model-based analysis

    R. Bhattacharyya, A. Burman, K. Singh, S. Banerjee, S. Maity, A. Auddy, S. K. Rout, S. Lahoti, R. Panda, and V. Baladandayuthapani

    BMJ Open 12(11) Paper

  4. RMExplorer: A visual analytics approach to explore the performance and fairness of disease risk models on population subgroups

    B. C. Kwon, U. Kartoun, S. Khurshid, M. Yurochkin, S. Maity, D. G. Brockman, A. V. Khera, P. T. Ellinor, S. A. Lubitz, and K. Ng

    IEEE VIS 2022 Paper

2021

  1. Statistical inference for individual fairness

    S. Maity, S. Xue, M. Yurochkin, and Y. Sun

    ICLR 2021 Paper

  2. Does enforcing fairness mitigate biases caused by subpopulation shift?

    S. Maity, D. Mukherjee, M. Yurochkin, and Y. Sun

    NeurIPS 2021 Paper