@booklet {somerstep2025carrotcostawarerate,
	title = {CARROT: A Cost Aware Rate Optimal Router},
	year = {2025},
	abstract = {With the rapid growth in the number of Large Language Models (LLMs), there has been a recent interest in LLM routing, or directing queries to the cheapest LLM that can deliver a suitable response. Following this line of work, we introduce CARROT, a Cost AwaRe Rate Optimal rouTer that can select models based on any desired trade-off between performance and cost. Given a query, CARROT selects a model based on estimates of models{\textquoteright} cost and performance. Its simplicity lends CARROT computational efficiency, while our theoretical analysis demonstrates minimax rate-optimality in its routing performance. Alongside CARROT, we also introduce the Smart Price-aware Routing (SPROUT) dataset to facilitate routing on a wide spectrum of queries with the latest state-of-the-art LLMs. Using SPROUT and prior benchmarks such as Routerbench and open-LLM-leaderboard-v2 we empirically validate CARROT{\textquoteright}s performance against several alternative routers.},
	url = {https://arxiv.org/abs/2502.03261},
	author = {Seamus Somerstep and Felipe Maia Polo and Allysson Flavio Melo de Oliveira and Prattyush Mangal and M{\'\i}rian Silva and Onkar Bhardwaj and Mikhail Yurochkin and Subha Maity}
}
@booklet {maity2025estimationmissingrandombinary,
	title = {Estimation with missing not at random binary outcomes via exponential tilts},
	year = {2025},
	abstract = {We study the problem of missing not at random (MNAR) datasets with binary outcomes. We propose an exponential tilt based approach that bypasses any knowledge on {\textquoteright}nonresponse instruments{\textquoteright} or {\textquoteright}shadow variables{\textquoteright} that are usually required for statistical estimation. We establish a sufficient condition for identifiability of tilt parameters and propose an algorithm to estimate them. Based on these tilt parameter estimates, we propose importance weighted and doubly robust estimators for any mean functions of interest, and validate their performances in a synthetic dataset. In an experiment with the Waterbirds dataset, we utilize our tilt framework to perform unsupervised transfer learning, when the responses are missing from a target domain of interest, and achieve a prediction performance that is comparable to a gold standard.},
	url = {https://arxiv.org/abs/2502.06046},
	author = {Subha Maity}
}
@article {maity_how_2022,
	title = {How does overparametrization affect performance on minority groups?},
	journal = {Transactions on Machine Learning Research},
	number = {arXiv:2206.03515},
	year = {2025},
	publisher = {arXiv},
	abstract = {The benefits of overparameterization for the overall performance of modern machine learning (ML) models are well known. However, the effect of overparameterization at a more granular level of data subgroups is less understood. Recent empirical studies demonstrate encouraging results: (i) when groups are not known, overparameterized models trained with empirical risk minimization (ERM) perform better on minority groups; (ii) when groups are known, ERM on data subsampled to equalize group sizes yields state-of-the-art worst-group accuracy in the overparameterized regime. In this paper, we complement these empirical studies with a theoretical investigation of the risk of overparameterized random feature regression models on minority groups with identical feature distribution as the majority group. In a setting in which the regression functions for the majority and minority groups are different, we show that overparameterization either improves or does not harm the asymptotic minority group performance under the ERM setting when the features are distributed uniformly over the sphere.},
	keywords = {Computer Science - Machine Learning, Mathematics - Statistics Theory, Statistics - Statistics Theory},
	doi = {10.48550/arXiv.2206.03515},
	url = {https://openreview.net/pdf?id=POunezXgvF},
	author = {Roy, Saptarshi and Maity, Subha and Xue, Songkai and Yurochkin, Mikhail and Sun, Yuekai}
}
@proceedings {bracale_learning_2024,
	title = {Learning the Distribution Map in Reverse Causal Performative Prediction},
	journal = {International Conference on Artificial Intelligence and Statistics},
	number = {arXiv:2405.15172},
	year = {2025},
	abstract = {In numerous predictive scenarios, the predictive model affects the sampling distribution; for example, job applicants often meticulously craft their resumes to navigate through a screening systems. Such shifts in distribution are particularly prevalent in the realm of social computing, yet, the strategies to learn these shifts from data remain remarkably limited. Inspired by a microeconomic model that adeptly characterizes agents{\textquoteright} behavior within labor markets, we introduce a novel approach to learn the distribution shift. Our method is predicated on a reverse causal model, wherein the predictive model instigates a distribution shift exclusively through a finite set of agents{\textquoteright} actions. Within this framework, we employ a microfoundation model for the agents{\textquoteright} actions and develop a statistically justified methodology to learn the distribution shift map, which we demonstrate to be effective in minimizing the performative prediction risk.},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	doi = {10.48550/arXiv.2405.15172},
	url = {http://arxiv.org/abs/2405.15172},
	author = {Bracale, Daniele and Maity, Subha and Banerjee, Moulinath and Sun, Yuekai}
}
@conference {36816,
	title = {Limitations of refinement methods for weak to strong generalization},
	booktitle = {Conference on Language Modeling (COLM)},
	year = {2025},
	url = {https://openreview.net/pdf?id=OKvSnV5Ar7},
	author = {Seamus Somerstep and Ya{\textquoteright}acov Ritov and Mikhail Yurochkin and Subha Maity and Yuekai Sun}
}
@proceedings {bracale_microfoundation_2024,
	title = {Microfoundation Inference for Strategic Prediction},
	journal = {International Conference on Artificial Intelligence and Statistics},
	number = {arXiv:2411.08998},
	year = {2025},
	publisher = {arXiv},
	abstract = {Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence stems from strategic actions taken by stakeholders with a vested interest in predictive models. A key challenge that hinders the widespread adaptation of performative prediction in machine learning is that practitioners are generally unaware of the social impacts of their predictions. To address this gap, we propose a methodology for learning the distribution map that encapsulates the long-term impacts of predictive models on the population. Specifically, we model agents{\textquoteright} responses as a cost-adjusted utility maximization problem and propose estimates for said cost. Our approach leverages optimal transport to align pre-model exposure (ex ante) and post-model exposure (ex post) distributions. We provide a rate of convergence for this proposed estimate and assess its quality through empirical demonstrations on a credit-scoring dataset.},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Statistics - Methodology},
	doi = {10.48550/arXiv.2411.08998},
	url = {https://proceedings.mlr.press/v258/bracale25a.html},
	author = {Bracale, Daniele and Maity, Subha and Polo, Felipe Maia and Somerstep, Seamus and Banerjee, Moulinath and Sun, Yuekai}
}
@article {36817,
	title = {Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach},
	year = {2025},
	url = {https://arxiv.org/abs/2509.22268},
	author = {Manli Cheng and Subha Maity and Qinglong Tian and Pengfei Li}
}
@conference {ngweta_aligners_2024,
	title = {Aligners: Decoupling LLMs and Alignment},
	booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
	year = {2024},
	pages = {13785{\textendash}13802},
	publisher = {Association for Computational Linguistics},
	organization = {Association for Computational Linguistics},
	abstract = {Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. Alignment is challenging, costly, and needs to be repeated for every LLM and alignment criterion. We propose to decouple LLMs and alignment by training *aligner* models that can be used to align any LLM for a given criteria on an as-needed basis, thus also reducing the potential negative impacts of alignment on performance. Our recipe for training the aligner models solely relies on synthetic data generated with a (prompted) LLM and can be easily adjusted for a variety of alignment criteria. We use the same synthetic data to train *inspectors*, binary miss-alignment classification models to guide a *squad* of multiple aligners. Our empirical results demonstrate consistent improvements when applying aligner squad to various LLMs, including chat-aligned models, across several instruction-following and red-teaming datasets.},
	doi = {10.18653/v1/2024.findings-emnlp.808},
	url = {https://aclanthology.org/2024.findings-emnlp.808},
	author = {Ngweta, Lilian and Agarwal, Mayank and Maity, Subha and Gittens, Alex and Sun, Yuekai and Yurochkin, Mikhail},
	editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung}
}
@conference {maity_investigation_2023,
	title = {An Investigation of Representation and Allocation Harms in Contrastive Learning},
	booktitle = {The Twelfth International Conference on Learning Representations},
	year = {2024},
	abstract = {The effect of underrepresentation on the performance of minority groups is known to be a serious problem in supervised learning settings; however, it has been underexplored so far in the context of self-supervised learning (SSL). In this paper, we demonstrate that contrastive learning (CL), a popular variant of SSL, tends to collapse representations of minority groups with certain majority groups. We refer to this phenomenon as representation harm and demonstrate it on image and text datasets using the corresponding popular CL methods. Furthermore, our causal mediation analysis of allocation harm on a downstream classification task reveals that representation harm is partly responsible for it, thus emphasizing the importance of studying and mitigating representation harm. Finally, we provide a theoretical explanation for representation harm using a stochastic block model that leads to a representational neural collapse in a contrastive learning setting.},
	url = {https://openreview.net/forum?id=q4SiDyYQbo},
	author = {Maity, Subha and Agarwal, Mayank and Yurochkin, Mikhail and Sun, Yuekai}
}
@article {maity_linear_2024,
	title = {A linear adjustment-based approach to posterior drift in transfer learning},
	journal = {Biometrika},
	volume = {111},
	number = {1},
	year = {2024},
	pages = {31{\textendash}50},
	abstract = {We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted for transfer learning problems in various domains including epidemiology, genetics and biomedicine. As concrete applications, we illustrate the power of our approach (i) through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data, and (ii) in overcoming a spurious correlation present in the source domain of the Waterbirds dataset.},
	issn = {1464-3510},
	doi = {10.1093/biomet/asad029},
	url = {https://doi.org/10.1093/biomet/asad029},
	author = {Maity, Subha and Dutta, Diptavo and Terhorst, Jonathan and Sun, Yuekai and Banerjee, Moulinath}
}
@conference {polo_weak_2024,
	title = {Weak Supervision Performance Evaluation via Partial Identification},
	booktitle = {The Thirty-eighth Annual Conference on Neural Information Processing Systems},
	year = {2024},
	abstract = {Programmatic Weak Supervision (PWS) enables supervised model training without direct access to ground truth labels, utilizing weak labels from heuristics, crowdsourcing, or pre-trained models. However, the absence of ground truth complicates model evaluation, as traditional metrics such as accuracy, precision, and recall cannot be directly calculated. In this work, we present a novel method to address this challenge by framing model evaluation as a partial identification problem and estimating performance bounds using Fr{\'e}chet bounds. Our approach derives reliable bounds on key metrics without requiring labeled data, overcoming core limitations in current weak supervision evaluation techniques. Through scalable convex optimization, we obtain accurate and computationally efficient bounds for metrics including accuracy, precision, recall, and F1-score, even in high-dimensional settings. This framework offers a robust approach to assessing model quality without ground truth labels, enhancing the practicality of weakly supervised learning for real-world applications.},
	url = {https://openreview.net/forum?id=VOVyeOzZx0},
	author = {Polo, Felipe Maia and Maity, Subha and Yurochkin, Mikhail and Banerjee, Moulinath and Sun, Yuekai}
}
@proceedings {bakshi_bayes_2023,
	title = {Bayes classifier cannot be learned from noisy responses with unknown noise rates},
	journal = {The Eleventh International Conference on Learning Representations, Tiny paper track},
	year = {2023},
	abstract = {Training a classifier with noisy labels typically requires the learner to specify the distribution of label noise, which is often unknown in practice. Although there have been some recent attempts to relax that requirement, we show that the Bayes decision rule is unidentified in most classification problems with noisy labels. This suggests it is generally not possible to bypass/relax the requirement. In the special cases in which the Bayes decision rule is identified, we develop a simple algorithm to learn the Bayes decision rule, that does not require knowledge of the noise distribution.},
	url = {https://openreview.net/forum?id=U4o5iSWSaD},
	author = {Bakshi, Soham and Maity, Subha}
}
@conference {maity_predictor-corrector_2022,
	title = {Predictor-corrector algorithms for stochastic optimization under gradual distribution shift},
	booktitle = {The Eleventh International Conference on Learning Representations},
	year = {2023},
	abstract = {Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g., gradual domain shift, object tracking, strategic classification). Often, the underlying process that drives the distribution shift is continuous in nature. We exploit this underlying continuity by developing predictor-corrector algorithms for time-varying stochastic optimization that anticipates changes in the underlying data generating process through a predictor-corrector term in the update rule. The key challenge is the estimation of the predictor-corrector term; a naive approach based on sample-average approximation may lead to non-convergence. We develop a general moving-average based method to estimate the predictor-corrector term and provide error bounds for the iterates, both in presence of pure and noisy access to the queries from the relevant derivatives of the loss function. Furthermore, we show (theoretically and empirically in several examples) that our method outperforms non-predictor corrector methods that do not anticipate changes in the data generating process.},
	url = {https://openreview.net/forum?id=2SV2dlfBuE3},
	author = {Maity, Subha and Mukherjee, Debarghya and Banerjee, Moulinath and Sun, Yuekai}
}
@conference {ngweta_simple_2023,
	title = {Simple Disentanglement of Style and Content in Visual Representations},
	booktitle = {Proceedings of the 40th International Conference on Machine Learning},
	volume = {202},
	year = {2023},
	note = {ISSN: 2640-3498},
	pages = {26063{\textendash}26086},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Learning visual representations with interpretable features, i.e., disentangled representations, remains a challenging problem. Existing methods demonstrate some success but are hard to apply to large-scale vision datasets like ImageNet. In this work, we propose a simple post-processing framework to disentangle content and style in learned representations from pre-trained vision models. We model the pre-trained features probabilistically as linearly entangled combinations of the latent content and style factors and develop a simple disentanglement algorithm based on the probabilistic model. We show that the method provably disentangles content and style features and verify its efficacy empirically. Our post-processed features yield significant domain generalization performance improvements when the distribution shift occurs due to style changes or style-related spurious correlations.},
	url = {https://proceedings.mlr.press/v202/ngweta23a.html},
	author = {Ngweta, Lilian and Maity, Subha and Gittens, Alex and Sun, Yuekai and Yurochkin, Mikhail}
}
@conference {maity_understanding_2022,
	title = {Understanding new tasks through the lens of training data via exponential tilting},
	booktitle = {The Eleventh International Conference on Learning Representations},
	year = {2023},
	abstract = {Deploying machine learning models on new tasks is a major challenge due to differences in distributions of the train (source) data and the new (target) data. However, the training data likely captures some of the properties of the new task. We consider the problem of reweighing the training samples to gain insights into the distribution of the target task. Specifically, we formulate a distribution shift model based on the exponential tilt assumption and learn train data importance weights minimizing the KL divergence between labeled train and unlabeled target datasets. The learned train data weights can then be used for downstream tasks such as target performance evaluation, fine-tuning, and model selection. We demonstrate the efficacy of our method on Waterbirds and Breeds benchmarks.},
	url = {https://openreview.net/forum?id=DBMttEEoLbw},
	author = {Maity, Subha and Yurochkin, Mikhail and Banerjee, Moulinath and Sun, Yuekai}
}
@article {maity_meta-analysis_2022,
	title = {Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions},
	journal = {Journal of Machine Learning Research},
	volume = {23},
	number = {198},
	year = {2022},
	pages = {1{\textendash}50},
	abstract = {We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. To borrow strength across such heterogeneous datasets, we introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We also propose a one-shot estimator of the global parameter that preserves the anonymity of the data sources and converges at a rate that depends on the size of the combined dataset. For high-dimensional linear model settings, we demonstrate the superiority of our identification restrictions in adapting to a previously seen data distribution as well as predicting for a new/unseen data distribution. Finally, we demonstrate the benefits of our approach on a large-scale drug treatment dataset involving several different cancer cell-lines.},
	issn = {1533-7928},
	url = {http://jmlr.org/papers/v23/21-0739.html},
	author = {Maity, Subha and Sun, Yuekai and Banerjee, Moulinath}
}
@article {maity_minimax_2022,
	title = {Minimax optimal approaches to the label shift problem in non-parametric settings},
	journal = {Journal of Machine Learning Research},
	volume = {23},
	number = {346},
	year = {2022},
	pages = {1{\textendash}45},
	abstract = {We study the minimax rates of the label shift problem in non-parametric classification. In addition to the unsupervised setting in which the learner only has access to unlabeled examples from the target domain, we also consider the setting in which a small number of labeled examples from the target domain is available to the learner. Our study reveals a difference in the difficulty of the label shift problem in the two settings, and we attribute this difference to the availability of data from the target domain to estimate the class conditional distributions in the latter setting. We also show that a class proportion estimation approach is minimax rate-optimal in the unsupervised setting.},
	issn = {1533-7928},
	url = {http://jmlr.org/papers/v23/21-1519.html},
	author = {Maity, Subha and Sun, Yuekai and Banerjee, Moulinath}
}
@conference {kwon_rmexplorer_2022,
	title = {RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups},
	booktitle = {IEEE Visualization and Visual Analytics (VIS)},
	year = {2022},
	note = {ISSN: 2771-9553},
	pages = {50{\textendash}54},
	abstract = {Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models de-veloped on one dataset may not generalize across diverse subpop-ulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to ex-plore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data.},
	keywords = {Analytical models, Atrial Fibrillation, Biological system modeling, Computational modeling, Data visualization, electronic health records, explainability, fairness, health informatics, Human-centered computing, interpretability, sociology, subgroup analysis, visual analytics, Visualization},
	doi = {10.1109/VIS54862.2022.00019},
	url = {https://ieeexplore.ieee.org/abstract/document/9973226},
	author = {Kwon, Bum Chul and Kartoun, Uri and Khurshid, Shaan and Yurochkin, Mikhail and Maity, Subha and Brockman, Deanna G and Khera, Amit V and Ellinor, Patrick T and Lubitz, Steven A and Ng, Kenney}
}
@article {bhattacharyya_role_2022,
	title = {Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis},
	journal = { BMJ Open},
	volume = {12},
	number = {11},
	year = {2022},
	note = {Publisher: British Medical Journal Publishing Group Section: Public health},
	pages = {e056292},
	abstract = {Objectives COVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19 has been a topic of interest, particularly in low-income and middle-income countries like India to assess its multifactorial impact on incidence, prevalence or mortality. This study aims to construct a statistical analysis pipeline to compute such vulnerability indices and investigate their association with metrics of the pandemic growth. Design Using publicly reported observational socioeconomic, demographic, health-based and epidemiological data from Indian national surveys, we compute contextual COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These cVIs are then used in Bayesian regression models to assess their impact on indicators of the spread of COVID-19. Setting This study uses district-level indicators and case counts data for the state of Odisha, India. Primary outcome measure We use instantaneous R (temporal average of estimated time-varying reproduction number for COVID-19) as the primary outcome variable in our models. Results Our observational study, focussing on 30 districts of Odisha, identified housing and hygiene conditions, COVID-19 preparedness and epidemiological factors as important indicators associated with COVID-19 vulnerability. Conclusion Having succeeded in containing COVID-19 to a reasonable level during the first wave, the second wave of COVID-19 made greater inroads into the hinterlands and peripheral districts of Odisha, burdening the already deficient public health system in these areas, as identified by the cVIs. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions, leading to more effective mitigation strategies for the present and future.},
	keywords = {covid-19, epidemiology, Public health},
	issn = {2044-6055, 2044-6055},
	doi = {10.1136/bmjopen-2021-056292},
	url = {https://bmjopen.bmj.com/content/12/11/e056292},
	author = {Bhattacharyya, Rupam and Burman, Anik and Singh, Kalpana and Banerjee, Sayantan and Maity, Subha and Auddy, Arnab and Rout, Sarit Kumar and Lahoti, Supriya and Panda, Rajmohan and Baladandayuthapani, Veerabhadran}
}
@conference {maity_does_2021,
	title = {Does enforcing fairness mitigate biases caused by subpopulation shift?},
	booktitle = {Advances in Neural Information Processing Systems},
	volume = {34},
	year = {2021},
	pages = {25773{\textendash}25784},
	publisher = {Curran Associates, Inc.},
	organization = {Curran Associates, Inc.},
	abstract = {Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we study whether enforcing algorithmic fairness during training improves the performance of the trained model in the \textbackslashemph{target domain}. On one hand, we conceive scenarios in which enforcing fairness does not improve performance in the target domain. In fact, it may even harm performance. On the other hand, we derive necessary and sufficient conditions under which enforcing algorithmic fairness leads to the Bayes model in the target domain. We also illustrate the practical implications of our theoretical results in simulations and on real data.},
	url = {https://proceedings.neurips.cc/paper/2021/hash/d800149d2f947ad4d64f34668f8b20f6-Abstract.html},
	author = {Maity, Subha and Mukherjee, Debarghya and Yurochkin, Mikhail and Sun, Yuekai}
}
@conference {maity_statistical_2020,
	title = {Statistical inference for individual fairness},
	booktitle = {The Ninth International Conference on Learning Representations},
	year = {2021},
	abstract = {As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating unwanted social biases has come to the fore of the public{\textquoteright}s and the research community{\textquoteright}s attention. In this paper, we focus on the problem of detecting violations of individual fairness in ML models. We formalize the problem as measuring the susceptibility of ML models against a form of adversarial attack and develop a suite of inference tools for the adversarial loss. The tools allow practitioners to assess the individual fairness of ML models in a statistically-principled way: form confidence intervals for the adversarial loss and test hypotheses of model fairness with (asymptotic) non-coverage/Type I error rate control. We demonstrate the utility of our tools in a real-world case study.},
	url = {https://openreview.net/forum?id=z9k8BWL-_2u},
	author = {Maity, Subha and Xue, Songkai and Yurochkin, Mikhail and Sun, Yuekai}
}
