Subha Maity - Publications

2025+

Maity, S. (2025+). Estimation with missing not at random binary outcomes via exponential tilts. (Submitted to JMLR). [preprint]
Somerstep, S.*, Maia Polo, F.*, Oliveira, A, Mangal, P., Silva, M., Bhardwaj, O., Yurochkin, M., & Maity, S. (2025+). CARROT: A Cost Aware Rate Optimal Router. (Submitted to ICML 2025). [preprint, huggingface]
Bracale, D.*, Maia Polo, F.*, Maity S.*, Somerstep, S.*, Banerjee, M., & Sun, Y. (2025+). Microfoundation inference for strategic prediction. (Accepted in AISTATS 2025). [preprint]
Bracale, D.*, Maity, S.*, Banerjee, M., & Sun Y. (2025+). Learning the distribution map in reverse causal in performative prediction. (Accepted in AISTATS 2025). [preprint]

Maia Polo, F.*, Maity, S.*, Banerjee M., Sun, Y., & Yurochkin, M. (2024). Weak supervision performance evaluation via partial identification. NeurIPS 2024. [paper]
Maity, S., Yurochkin, M., & Sun, Y. (2024). An investigation of allocation and representation harms in contrastive learning. ICLR 2024. [paper]
Ngweta, L.*, Maity, S.*, Agarwal, M., Gittens, A., Sun, Y., & Yurochkin, M. (2024). Aligners: Decoupling LLMs and Alignment. Conference on Empirical Methods in Natural Language Processing 2024. [paper]
Maity, S., Dutta, D., Terhorst, J., Sun, Y., & Banerjee, M. (2024). A linear adjustment based approach to posterior drift in transfer learning. Biometrika, 2024. [paper]

Maity, S., Yurochkin, M., Banerjee, M., & Sun, Y. (2023). Understanding new tasks through the lens of training data via exponential tilting. In The Eleventh International Conference on Learning Representations, 2023. [paper]
Maity, S.*, Mukherjee, D.*, Banerjee, M., & Sun, Y. (2023). Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. In The Eleventh International Conference on Learning Representations, 2023. [paper]
Bakshi, S.*, & Maity, S.* (2023). Bayes classifier cannot be learned from noisy responses with unknown noise rates. Accepted as Tiny paper in the Eleventh International Conference on Learning Representations, 2023. [paper]
Ngweta, L.*, Maity, S.*, Gittens, A., Sun, Y., & Yurochkin, M. (2023). Simple disentanglement of style and content in visual representations. In Fortieth International Conference on Machine Learning, 2023. [paper]

Maity, S., Sun, Y., & Banerjee, M. (2022). Minimax optimal approaches to the label shift problem in non-parametric settings. Journal of Machine Learning Research, 23(346), 1−45. [paper]
Maity, S., Sun, Y., & Banerjee, M. (2022). Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions. Journal of Machine Learning Research, 23(198), 1-50. [paper]
Kwon, B. C., Kartoun, U., Khurshid, S., Yurochkin, M., Maity, S., Brockman, D. G., ... & Ng, K. (2022). RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups. In 2022 IEEE Visualization and Visual Analytics (VIS) (pp. 50-54). IEEE. [paper]
Bhattacharyya, R., Burman, A., Singh, K., Banerjee, S., Maity, S., Auddy, A., ... & Baladandayuthapani, V. (2022). Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis. BMJ Open, 12(11), e056292. [paper]

Maity, S.*, Xue, S.*, Yurochkin, M., & Sun, Y. (2021). Statistical inference for individual fairness. In International Conference on Learning Representations, 2021. [paper]
Maity, S.*, Mukherjee, D.*, Yurochkin, M., & Sun, Y. (2021). Does enforcing fairness mitigate biases caused by subpopulation shift?. In NeurIPS 2021. [paper]