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Shiv Kumar Tavker

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3 papers

1 author row

TMLR Journal 2026 Journal Article

Probabilistic Pretraining for Improved Neural Regression

Boris N. Oreshkin
Shiv Kumar Tavker
Dmitry Efimov

While transfer learning has revolutionized computer vision and natural language processing, its application to probabilistic regression remains underexplored, particularly for tabular data. We introduce NIAQUE (Neural Interpretable Any-Quantile Estimation), a novel permutation-invariant architecture that enables effective transfer learning across diverse regression tasks. Through extensive experiments on 101 datasets, we demonstrate that pre-training NIAQUE on multiple datasets and fine-tuning on target datasets consistently outperforms both traditional tree-based models and transformer-based neural baseline. On real-world Kaggle competitions, NIAQUE achieves competitive performance against heavily hand-crafted and feature-engineered solutions and outperforms strong baselines such as TabPFN and TabDPT, while maintaining interpretability through its probabilistic framework. Our results establish NIAQUE as a robust and scalable approach for tabular regression, effectively bridging the gap between traditional methods and modern transfer learning.

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JMLR Journal 2024 Journal Article

Consistent Multiclass Algorithms for Complex Metrics and Constraints

Harikrishna Narasimhan
Harish G. Ramaswamy
Shiv Kumar Tavker
Drona Khurana
Praneeth Netrapalli
Shivani Agarwal

We present consistent algorithms for multiclass learning with complex performance metrics and constraints, where the objective and constraints are defined by arbitrary functions of the confusion matrix. This setting includes many common performance metrics such as the multiclass G-mean and micro F-measure, and constraints such as those on the classifier's precision and recall and more recent measures of fairness discrepancy. We give a general framework for designing consistent algorithms for such complex design goals by viewing the learning problem as an optimization problem over the set of feasible confusion matrices. We provide multiple instantiations of our framework under different assumptions on the performance metrics and constraints, and in each case show rates of convergence to the optimal (feasible) classifier (and thus asymptotic consistency). Experiments on a variety of multiclass classification tasks and fairness constrained problems show that our algorithms compare favorably to the state-of-the-art baselines. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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NeurIPS Conference 2020 Conference Paper

Consistent Plug-in Classifiers for Complex Objectives and Constraints

Shiv Kumar Tavker
Harish Guruprasad Ramaswamy
Harikrishna Narasimhan

We present a statistically consistent algorithm for constrained classification problems where the objective (e. g. F-measure, G-mean) and the constraints (e. g. demographic parity, coverage) are defined by general functions of the confusion matrix. The key idea is to reduce the problem into a sequence of plug-in classifier learning problems, which is done by formulating an optimization problem over the intersection of the set of achievable confusion matrices and the set of feasible matrices. For objective and constraints that are convex functions of the confusion matrix, our algorithm requires $O(1/\epsilon^2)$ calls to the plug-in routine, which improves on the $O(1/\epsilon^3)$ rate achieved by Narasimhan (2018). We demonstrate empirically that our algorithm performs at least as well as the state-of-the-art methods for these problems.

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