Feel free to refer my Google Scholar Profile as well.

  1. Epidemiology of Large Language Models: A Benchmark for Observational Distribution Knowledge

    Plecko, D., Okanovic, P, Havaldar, S., Hoefler, T. & Bareinboim, E.

    [Paper] (under submission at DMLR)

  2. Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation

    Havaldar, S., Sharma, N., Sareen, S., Shanmugam, K., & Raghuveer, A.

    [Paper] (Poster @ ICLR ‘24, Oral @ Regulatable-ML @ NeurIPS ‘23)

  3. Improving Fairness-Accuracy tradeoff with few Test Samples under Covariate Shift

    Havaldar, S., Chauhan, J., Shanmugam, K., Nandy, J., & Raghuveer, A.

    [Paper] (Poster @ AAAI ‘24 & Spotlight @ Algorithmic Fairness through the Lens of Time @ NeurIPS ‘23)

  4. Label Differential Privacy via Aggregation

    Brahmbhatt, A., Saket, R., Havaldar, S., Nasery, A., & Raghuveer, A.

    [Paper] (under submission at TMLR)

  5. On the Benefits of Defining Vicinal Distributions in Latent Space

    Mangla P., Singh V., Havaldar, S., & Balasubramanian V.,

    [Paper] Best Paper Award @ AML-CV Workshop @ (CVPR’21) (Also presented at 2 Workshops on RobustML and Responsible AI at ICLR’21)