Trainees are indicated as (*)
2026+
- Chakraborty, A., & Maity, S. (2026). The Statistical Cost of Adaptation in Multi-Source Transfer Learning. (Submitted to JMLR) [preprint]
- Xu, M.(*), Maity, S., & Dubin, J. (2026). Robust inference for risk heterogeneity under group imbalance. (To be submitted to Biometrics) [preprint]
2025
- Cheng, M.(*), Maity, S., Tian, Q., & Li, P. (2025). Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach. (Major revision in Scandinivian Journal of Statistics) [preprint]
- Somerstep, S., Polo, F.M., de Oliveira, A.F.M., Mangal, P., Silva, M., Bhardwaj, O., Yurochkin, M. and Maity, S. (2025). Carrot: A cost aware rate optimal router. (Submitted for a review in the Annals of Applied Statistics after major revision) [preprint]
- Xu, M.(*), Maity, S., & Dubin, J. (2025). Diagnosis-based mortality prediction for intensive care unit patients via transfer learning. (To be submitted to Canadian Journal of Statistics) [preprint]
- Bracale, D., Maity, S., Banerjee, M., & Sun, Y. (2025). Learning the distribution map in reverse causal performative prediction. AISTAT. [paper]
- Somerstep, S., Ritov, Y. A., Yurochkin, M., Maity, S., & Sun, Y. (2025). Limitations of refinement methods for weak to strong generalization. COLM. [paper]
- Bracale, D., Maity, S., Polo, F. M., Somerstep, S., Banerjee, M., & Sun, Y. (2025). Microfoundation inference for strategic prediction. AISTAT. [paper]
2024
- Maity, S., Dutta, D., Terhorst, J., Sun, Y., & Banerjee, M. (2024). A linear adjustment-based approach to posterior drift in transfer learning. Biometrika, 111(1), 31-50. [paper]
- Maia Polo, F., Maity, S., Yurochkin, M., Banerjee, M., & Sun, Y. (2024). Weak supervision performance evaluation via partial identification. NeurIPS. [paper]
- Maity, S., Agarwal, M., Yurochkin, M., & Sun, Y. (2024, May). An investigation of representation and allocation harms in contrastive learning. ICLR. [paper]
- Ngweta, L., Agarwal, M., Maity, S., Gittens, A., Sun, Y., & Yurochkin, M. (2024, November). Aligners: Decoupling llms and alignment. EMNLP. [paper]
2023
- Maity, S., Yurochkin, M., Banerjee, M., & Sun, Y. (2022). Understanding new tasks through the lens of training data via exponential tilting. ICLR. [paper]
- Maity, S., Mukherjee, D., Banerjee, M., & Sun, Y. (2022). Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. ICLR. [paper]
- Ngweta, L., Maity, S., Gittens, A., Sun, Y., & Yurochkin, M. (2023). Simple disentanglement of style and content in visual representations. ICML. [paper]
- Bakshi, S., & Maity, S. (2023). Bayes classifier cannot be learned from noisy responses with unknown noise rates. ICLR Tiny Paper. [paper]
2022
- 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]
- Bhattacharyya, R., Burman, A., Singh, K., Banerjee, S., Maity, S., Auddy, A., Rout, S.K., Lahoti, S., Panda, R. and Baladandayuthapani, V. (2022). Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis. BMJ open, 12(11), p.e056292. [paper]
- Kwon, B.C., Kartoun, U., Khurshid, S., Yurochkin, M., Maity, S., Brockman, D.G., Khera, A.V., Ellinor, P.T., Lubitz, S.A. and 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]