Publications

I would like to thank the National Science Foundation, Office of Naval Research (including a Young Investigator Award), IBM, Liberty Mutual Insurance, Google Research, Takeda, and MIT (JFRAP, RSC) for supporting my research group. In the past (Columbia University) our research was funded by the Betty-Moore Sloan foundation.

A more complete list of publications can also be found at: Google Scholar and ArXiv

Some Papers in Pipeline

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder and Peter Radchenko 2025+
Journal of the American Statistical Association (major revision)
Univariate convex regression: $\ell_q$ risk bounds under heavy-tailed noise
Rahul Mazumder and Haoyue Wang 2025+
Mathematics of Operations Research (major revision)
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
Kayhan Behdin, Gabriel Loewinger, Kenneth T. Kishida, Giovanni Parmigiani, Rahul Mazumder, 2025+
Journal of the Royal Statistical Society, Series B (major revision)
Modeling with Categorical Features via Exact Fusion and Sparsity Regularization
Kayhan Behdin, Riade Benbaki, Peter Radchenko, Rahul Mazumder, 2025+
Journal of the Royal Statistical Society, Series B (major revision)
Locally Transparent Rule Sets for Explainable Machine Learning
Brian Liu and Rahul Mazumder, 2025+
Operations Research (R&R)
Improved heritability partitioning and enrichment analyses using summary statistics with graphREML
Hui Li, Tushar Kamath, Rahul Mazumder, Xihong Lin, and Luke O’Connor, 2025+
SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations
Tony Chen, Haoyu Zhang, Rahul Mazumder, and Xihong Lin, 2025+

Journal Publications

Sparse PCA: A New Scalable Estimator Based On Integer Programming
Kayhan Behdin and Rahul Mazumder, 2025+
Annals of Statistics (forthcoming)
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
Brian Liu and Rahul Mazumder, 2025
Journal of Machine Learning Research (forthcoming)
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
Kayhan Behdin, Wenyu Chen, and Rahul Mazumder, 2025+
Operations Research (minor revision)
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach
Haoyue Wang, Shibal Ibrahim, and Rahul Mazumder, 2025
SIAM Journal on Mathematics of Data Science
Predicting Census Survey Response Rates with Parsimonious Additive Models and Structured Interactions
Shibal Ibrahim, Peter Radchenko, Emanuel Ben-David, and Rahul Mazumder, 2025
Annals of Applied Statistics
PolyCD: Optimization via Cycling through the Vertices of a Polytope
Rahul Mazumder and Haoyue Wang, 2024
SIAM Journal on Optimization
Sparse NMF with Archetypal Regularization: Computational and Robustness Properties
Kayhan Behdin, and Rahul Mazumder, 2024
Journal of Machine Learning Research
Subgradient Regularized Multivariate Convex Regression at Scale
Wenyu Chen and Rahul Mazumder, 2024
SIAM Journal on Optimization
Fast and scalable ensemble learning method for versatile polygenic risk prediction
Tony Chen, Haoyu Zhang, Rahul Mazumder, Xihong Lin, 2024
Proceedings of the National Academy of Sciences
Optimal ensemble construction for multistudy prediction with applications to mortality estimation
Gabriel Loewinger, Rolando Acosta Nunez, Rahul Mazumder, and Giovanni Parmigiani, 2024
Statistics in Medicine
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
Hussein Hazimeh, Rahul Mazumder and Tim Nonet, 2023
Journal of Machine Learning Research
A New Computational Framework for Log-Concave Density Estimation
Wenyu Chen, Rahul Mazumder and Richard J. Samworth, 2023
Mathematical Programming Computation
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives
Hussein Hazimeh, Rahul Mazumder and Peter Radchenko, 2023
Annals of Statistics
Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low
Rahul Mazumder, Peter Radchenko and Antoine Dedieu, 2023
Operations Research
Linear regression with partially mismatched data: local search with theoretical guarantees
Rahul Mazumder and Haoyue Wang, 2023
Mathematical Programming
(An extended abstract of this paper appeared in IPCO)
Accurate and Efficient Estimation of Local Heritability using Summary Statistics and LD Matrix
Hui Li, Rahul Mazumder, Xihong Lin, 2023
Nature Communications
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning
Haoyue Wang, Haihao Lu and Rahul Mazumder, 2022
SIAM Journal on Optimization
Solving Ll-regularized SVMs and related linear programs: Revisiting the effectiveness of Column and Constraint Generation
Antoine Dedieu, Rahul Mazumder and Haoyue Wang, 2022
Journal of Machine Learning Research
Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization
Hussein Hazimeh, Rahul Mazumder and Ali Saab, 2022
Mathematical Programming
--MIT Operations Research Center Best Student Paper Award, 2020. (Awardee: Hazimeh)
Using L1-relaxation and integer programming to obtain dual bounds for sparse PCA
Santanu Dey, Rahul Mazumder and Guanyi Wang, 2021.
Operations Research
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
Antoine Dedieu, Hussein Hazimeh and Rahul Mazumder, 2021
Journal of Machine Learning Research
Integration of Survival Data from Multiple Studies
Steffen Ventz, Rahul Mazumder and Lorenzo Trippa, 2021
Biometrics
Mining Events with Declassified Diplomatic Documents
Yuanjun Gao, Jack Goetz, Matthew Connelly and Rahul Mazumder, 2020
Annals of Applied Statistics
Randomized Gradient Boosting Machine
Haihao Lu and Rahul Mazumder, 2020
SIAM Journal on Optimization
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
Hussein Hazimeh and Rahul Mazumder, 2020
Operations Research
--INFORMS Optimization Society Young Researchers Prize, 2020. (Awardees: Hazimeh and Mazumder)
Computing the degrees of freedom of rank-regularized estimators and cousins
Rahul Mazumder and Haolei Weng, 2020
Electronic Journal of Statistics
Matrix completion with nonconvex regularization: spectral operators and scalable algorithms
Rahul Mazumder, Diego Saldana and Haolei Weng, 2020
Statistics and Computing
Computation of the Maximum Likelihood estimator in low-rank Factor Analysis
Koulik Khamaru and Rahul Mazumder, 2019
Mathematical Programming
A Computational Framework for Multivariate Convex Regression and its Variants
Rahul Mazumder, Arkopal Choudhury, Garud Iyengar and Bodhisattva Sen, 2019
Journal of the American Statistical Association, Theory and Methods
Learning a Mixture of Gaussians via Mixed Integer Optimization
Hari Bandi, Dimitris Bertsimas and Rahul Mazumder, 2019
Informs Journal on Optimization
Flexible low-rank statistical modeling with missing data and side information
William Fithian and Rahul Mazumder, 2018
Statistical Science
Certifiably Optimal Low Rank Factor Analysis
Dimitris Bertsimas, Martin Copenhayer and Rahul Mazumder, 2017
Journal of Machine Learning Research
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
Rahul Mazumder and Peter Radchenko, 2017
IEEE Transactions on Information Theory
An Extended Frank-Wolfe Method with In-Face Directions, and its Application to Low-Rank Matrix Completion
Robert Freund, Paul Grigas and Rahul Mazumder, 2017
SIAM Journal on Optimization
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
Robert Freund, Paul Grigas and Rahul Mazumder, 2017
Annals of Statistics
--Special Invited Session at the Joint Statistical Meetings, 2017
--INFORMS Optimization Society Student Paper Award, 2015. (Awardee Grigas)
Best Subset Selection via a Modern Optimization Lens
Dimitris Bertsimas, Angela King and Rahul Mazumder, 2016
Annals of Statistics
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
Trevor Hastie’, Rahul Mazumder’, Jason Lee and Reza Zadeh, 2015
Journal of Machine Learning Research
Least Quantile of Squares Regression via Modern Optimization
Dimitris Bertsimas and Rahul Mazumder, 2014
Annals of Statistics
Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach
Julia Validomat, Rahul Mazumder, Alex McInturff, Douglas McCauley and Trevor Hastie, 2014
Biometrics
The Graphical Lasso: New Insights and Alternatives
Rahul Mazumder and Trevor Hastie, 2012
Electronic Journal of Statistics
Exact covariance thresholding into connected components for large-scale Graphical Lasso
Rahul Mazumder and Trevor Hastie, 2012
Journal of Machine Learning Research
SparseNet: Coordinate Descent with Non-Convex Penalties
Rahul Mazumder, Jerome Friedman and Trevor Hastie, 2011
Journal of American Statistical Association, Theory and Methods
Modeling Item-Item Similarities for Personalized Recommendations on Yahoo! Front Page
Deepak Agarwal, Liang Zhang and Rahul Mazumder, 2011
Annals of Applied Statistics
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
Rahul Mazumder, Trevor Hastie and Robert Tibshirani, 2010
Journal of Machine Learning Research

Conference Publications

An Optimization Framework for Differentially Private Sparse Fine-Tuning
Mehdi Makni, Kayhan Behdin, Gabriel Afriat, Zheng Xu, Sergei Vassilvitskii, Natalia Ponomareva, Hussein Hazimeh, and Rahul Mazumder, 2025
SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’25)
MOSS: Multi-Objective Optimization for Stable Rule Sets
Brian Liu and Rahul Mazumder, 2025
SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’25)
-- Recipient of INFORMS Data Mining Best Student Paper Award, 2024 (first place)
A unified framework for Sparse plus Low-Rank Matrix Decomposition for LLMs
Mehdi Makni, Kayhan Behdin, Zheng Xu, Natalia Ponomareva, and Rahul Mazumder, 2025
The Second Conference on Parsimony and Learning (CPAL 25)
-- Selected for Oral Presentation
Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework
Ryan Lucas and Rahul Mazumder, 2025
International Conference on Learning Representations (ICLR 25)
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models
Xiang Meng, Kayhan Behdin, Haoyue Wang, and Rahul Mazumder, 2024
Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS ’24)
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
Brian Liu and Rahul Mazumder 2024
30th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 24)
-- 2025 American Statistical Association Statistical Computing Student Paper Competition Award Winner
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization
Xiang Meng, Shibal Ibrahim, Kayhan Behdin, Hussein Hazimeh, Natalia Ponomareva, and Rahul Mazumder, 2024
International Conference on Machine Learning (ICML ’24)
End-to-end Feature Selection Approach for Learning Skinny Trees
Shibal Ibrahim, Kayhan Behdin, and Rahul Mazumder, 2024
International Conference on Artificial Intelligence and Statistics (AISTATS 24)
-- Recipient of Student Paper Highlight Award
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
Xiang Meng, Wenyu Chen, Riade Benbaki and Rahul Mazumder
International Conference on Artificial Intelligence and Statistics (AISTATS ’24)
Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction
Shibal Ibrahim, Max Tell and Rahul Mazumder, 2023
4th ACM International Conference on AI in Finance (ICAIF 23)
Dynamic Covariance Estimation under Structural Assumptions via a Joint Optimization Approach
Riade Benbaki, Wenyu Chen, Yada Zhu and Rahul Mazumder, 2023
4th ACM International Conference on AI in Finance (ICAIF 23)
GRAND-SLAMIN’ Interpretable Additive Modeling with Structural Constraints
Shibal Ibrahim, Gabriel Isaac Afriat, Kayhan Behdin and Rahul Mazumder, 2023
Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 23)
On the Convergence of CART under Sufficient Impurity Decrease Condition
Rahul Mazumder and Haoyue Wang, 2023
Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 23)
Optimizing for Member Value in an Edge Building Marketplace
Ayan Acharya, Siyuan Gao, Ankan Saha, Borja Ocejo, Kinjal Basu, Keerthi Selvaraj, Rahul Mazumder, Aman Gupta and Parag Agrawal, 2023
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 23)
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder, 2023
29th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 23)
FIRE: An Optimization Framework for Fast Interpretable Rule Extraction
Brian Liu and Rahul Mazumder, 2023
29th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 23)
ForestPrune: Compact Depth-Pruned Tree Ensembles
Brian Liu and Rahul Mazumder, 2023
International Conference on Artificial Intelligence and Statistics (AISTATS 23)
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder, 2023
Proceedings of the 40th International Conference on Machine Learning (ICML 23)
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu, 2022
Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 22)
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles
Shibal Ibrahim, Hussein Hazimeh, and Rahul Mazumder, 2022
28th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 22)
-- KDD Best Student Paper Award 2022 (Awardee: Ibrahim).
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Rahul Mazumder, Xiang Meng, Haoyue Wang, 2022
Proceedings of the 39th International Conference on Machine Learning (ICML 22)
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance
Shibal Ibrahim, Natalia Ponomareva and Rahul Mazumder, 2022
ECML PKDD 2022
Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series
Shibal Ibrahim, Wenyu Chen, Yada Zhu, Pin-Yu Chen, Yang Zhang, Rahul Mazumder, 2022
3rd ACM International Conference on AI in Finance (ICAIF 22)
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul
Mazumder, Lichan Hong and Ed H. Chi, 2021
Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 21)
Linear Regression with Mismatched Data: a Provably Optimal Local Search Algorithm
Rahul Mazumder and Haoyue Wang, 2021
Proceedings of the 22nd International Conference on Integer Programming and Combinatorial Optimization (IPCO 21)
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Kinjal Basu, Amol Ghoting, Rahul Mazumder and Yao Pan, 2020
Proceedings of the 37th International Conference of Machine Learning (ICML 20)
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan and Rahul Mazumder, 2020
Proceedings of the 37th International Conference on Machine Learning (ICML 20)
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
Hussein Hazimeh and Rahul Mazumder, 2020
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 20)