Spectral Regularization Algorithms for Learning Large Incomplete Matrices. - Rahul Mazumder; Trevor Hastie; Robert Tibshirani (J. Mach. Learn. Res., 2010)

The Graphical Lasso: New Insights and Alternatives - Rahul Mazumder; Trevor Hastie (CoRR, 2011)

Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. - Rahul Mazumder; Trevor Hastie (J. Mach. Learn. Res., 2012)

Non-negative matrix completion for bandwidth extension: A convex optimization approach. - Dennis L. Sun; Rahul Mazumder (MLSP, 2013)

AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods. - Robert M. Freund; Paul Grigas; Rahul Mazumder (CoRR, 2013)

A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives. - Robert M. Freund; Paul Grigas; Rahul Mazumder (CoRR, 2015)

Matrix completion and low-rank SVD via fast alternating least squares. - Trevor Hastie; Rahul Mazumder; Jason D. Lee; Reza Zadeh (J. Mach. Learn. Res., 2015)

Certifiably Optimal Low Rank Factor Analysis. - Dimitris Bertsimas; Martin S. Copenhaver; Rahul Mazumder (J. Mach. Learn. Res., 2017)

An Extended Frank-Wolfe Method with "In-Face" Directions, and Its Application to Low-Rank Matrix Completion. - Robert M. Freund; Paul Grigas; Rahul Mazumder (SIAM J. Optim., 2017)

The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization. - Rahul Mazumder; Peter Radchenko (IEEE Trans. Inf. Theory, 2017)

Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming. - Antoine Dedieu; Rahul Mazumder; Zhen Zhu; Hossein Vahabi (CIKM, 2018)

Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming. - Antoine Dedieu; Rahul Mazumder; Zhen Zhu; Hossein Vahabi (CoRR, 2018)

Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods. - Robert M. Freund; Paul Grigas; Rahul Mazumder (CoRR, 2018)

Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation. - Antoine Dedieu; Rahul Mazumder (CoRR, 2019)

Computation of the maximum likelihood estimator in low-rank factor analysis. - Koulik Khamaru; Rahul Mazumder (Math. Program., 2019)

Learning a Mixture of Gaussians via Mixed-Integer Optimization. - Hari Bandi; Dimitris Bertsimas; Rahul Mazumder (INFORMS J. Optim., 2019)

Learning Hierarchical Interactions at Scale: A Convex Optimization Approach. - Hussein Hazimeh 001; Rahul Mazumder (AISTATS, 2020)

ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications. - Kinjal Basu 001; Amol Ghoting; Rahul Mazumder; Yao Pan (ICML, 2020)

The Tree Ensemble Layer: Differentiability meets Conditional Computation. - Hussein Hazimeh 001; Natalia Ponomareva 001; Petros Mol; Zhenyu Tan; Rahul Mazumder (ICML, 2020)

Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms. - Hussein Hazimeh 001; Rahul Mazumder (Oper. Res., 2020)

Matrix completion with nonconvex regularization: spectral operators and scalable algorithms. - Rahul Mazumder; Diego Saldana; Haolei Weng (Stat. Comput., 2020)

Randomized Gradient Boosting Machine. - Haihao Lu; Rahul Mazumder (SIAM J. Optim., 2020)

Linear Regression with Mismatched Data: A Provably Optimal Local Search Algorithm. - Rahul Mazumder; Haoyue Wang (IPCO, 2021)

DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. - Hussein Hazimeh 001; Zhe Zhao 001; Aakanksha Chowdhery; Maheswaran Sathiamoorthy; Yihua Chen; Rahul Mazumder; Lichan Hong; Ed H. Chi (NeurIPS, 2021)

Archetypal Analysis for Sparse Nonnegative Matrix Factorization: Robustness Under Misspecification. - Kayhan Behdin; Rahul Mazumder (CoRR, 2021)

Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives. - Hussein Hazimeh 001; Rahul Mazumder; Peter Radchenko (CoRR, 2021)

Predicting Census Survey Response Rates via Interpretable Nonparametric Additive Models with Structured Interactions. - Shibal Ibrahim; Rahul Mazumder; Peter Radchenko; Emanuel Ben-David (CoRR, 2021)

Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation. - Gabriel Loewinger; Rolando Acosta Nunez; Rahul Mazumder; Giovanni Parmigiani (CoRR, 2021)

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives. - Antoine Dedieu; Hussein Hazimeh 001; Rahul Mazumder (J. Mach. Learn. Res., 2021)

Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series. - Shibal Ibrahim; Wenyu Chen 003; Yada Zhu; Pin-Yu Chen; Yang Zhang 001; Rahul Mazumder (ICAIF, 2022)

Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features. - Rahul Mazumder; Xiang Meng 004; Haoyue Wang (ICML, 2022)

Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles. - Shibal Ibrahim; Hussein Hazimeh 001; Rahul Mazumder (KDD, 2022)

Pushing the limits of fairness impossibility: Who's the fairest of them all? - Brian Hsu; Rahul Mazumder; Preetam Nandy; Kinjal Basu 001 (NeurIPS, 2022)

Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance. - Shibal Ibrahim; Natalia Ponomareva 001; Rahul Mazumder (ECML/PKDD (1), 2022)

ForestPrune: Compact Depth-Controlled Tree Ensembles. - Brian Liu 002; Rahul Mazumder (CoRR, 2022)

Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization. - Kayhan Behdin; Qingquan Song; Aman Gupta; David Durfee; Ayan Acharya; S. Sathiya Keerthi; Rahul Mazumder (CoRR, 2022)

Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA. - Santanu S. Dey; Rahul Mazumder; Guanyi Wang (Oper. Res., 2022)

Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation. - Antoine Dedieu; Rahul Mazumder; Haoyue Wang (J. Mach. Learn. Res., 2022)

Sparse regression at scale: branch-and-bound rooted in first-order optimization. - Hussein Hazimeh 001; Rahul Mazumder; Ali Saab (Math. Program., 2022)

Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning. - Haoyue Wang; Haihao Lu; Rahul Mazumder (SIAM J. Optim., 2022)

ForestPrune: Compact Depth-Pruned Tree Ensembles. - Brian Liu 002; Rahul Mazumder (AISTATS, 2023)

Optimizing for Member Value in an Edge Building Marketplace. - Ayan Acharya; Siyuan Gao; Ankan Saha; Borja Ocejo; Kinjal Basu 001; Sathiya Keerthi Selvaraj; Rahul Mazumder; Aman Gupta; Parag Agrawal (CIKM, 2023)

Dynamic Covariance Estimation under Structural Assumptions via a Joint Optimization Approach. - Wenyu Chen 003; Riade Benbaki; Yada Zhu; Rahul Mazumder (ICAIF, 2023)

Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction. - Shibal Ibrahim; Max Tell; Rahul Mazumder (ICAIF, 2023)

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. - Riade Benbaki; Wenyu Chen 003; Xiang Meng 004; Hussein Hazimeh 001; Natalia Ponomareva 001; Zhe Zhao 001; Rahul Mazumder (ICML, 2023)

Practical Design of Performant Recommender Systems using Large-scale Linear Programming-based Global Inference. - Aman Gupta; S. Sathiya Keerthi; Ayan Acharya; Miao Cheng; Borja Ocejo Elizondo; Rohan Ramanath; Rahul Mazumder; Kinjal Basu 001; J. Kenneth Tay; Rupesh Gupta (KDD, 2023)

COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search. - Shibal Ibrahim; Wenyu Chen 003; Hussein Hazimeh 001; Natalia Ponomareva 001; Zhe Zhao 001; Rahul Mazumder (KDD, 2023)

Fire: An Optimization Approach for Fast Interpretable Rule Extraction. - Brian Liu 002; Rahul Mazumder (KDD, 2023)

GRAND-SLAMIN' Interpretable Additive Modeling with Structural Constraints. - Shibal Ibrahim; Gabriel Afriat; Kayhan Behdin; Rahul Mazumder (NeurIPS, 2023)

On the Convergence of CART under Sufficient Impurity Decrease Condition. - Rahul Mazumder; Haoyue Wang (NeurIPS, 2023)

Promoting Inactive Members in Edge-Building Marketplace. - Ayan Acharya; Siyuan Gao; Borja Ocejo; Kinjal Basu 001; Ankan Saha; Sathiya Keerthi Selvaraj; Rahul Mazumder; Parag Agrawal; Aman Gupta (WWW (Companion Volume), 2023)

mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization. - Kayhan Behdin; Qingquan Song; Aman Gupta; Ayan Acharya; David Durfee; Borja Ocejo; S. Sathiya Keerthi; Rahul Mazumder (CoRR, 2023)

Sharpness-Aware Minimization: An Implicit Regularization Perspective. - Kayhan Behdin; Rahul Mazumder (CoRR, 2023)

Matrix Completion from General Deterministic Sampling Patterns. - Hanbyul Lee 001; Rahul Mazumder; Qifan Song; Jean Honorio (CoRR, 2023)

Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives. - Kayhan Behdin; Wenyu Chen 003; Rahul Mazumder (CoRR, 2023)

QuantEase: Optimization-based Quantization for Language Models - An Efficient and Intuitive Algorithm. - Kayhan Behdin; Ayan Acharya; Aman Gupta; Sathiya Keerthi Selvaraj; Rahul Mazumder (CoRR, 2023)

L0Learn: A Scalable Package for Sparse Learning using L0 Regularization. - Hussein Hazimeh 001; Rahul Mazumder; Tim Nonet (J. Mach. Learn. Res., 2023)

Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low. - Rahul Mazumder; Peter Radchenko; Antoine Dedieu (Oper. Res., 2023)

Linear regression with partially mismatched data: local search with theoretical guarantees. - Rahul Mazumder; Haoyue Wang (Math. Program., 2023)

End-to-end Feature Selection Approach for Learning Skinny Trees. - Shibal Ibrahim; Kayhan Behdin; Rahul Mazumder (AISTATS, 2024)

FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning. - Xiang Meng 004; Wenyu Chen 003; Riade Benbaki; Rahul Mazumder (AISTATS, 2024)

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization. - Xiang Meng 004; Shibal Ibrahim; Kayhan Behdin; Hussein Hazimeh 001; Natalia Ponomareva 001; Rahul Mazumder (ICML, 2024)

FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML. - Brian Liu 002; Rahul Mazumder (KDD, 2024)

ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models. - Xiang Meng 004; Kayhan Behdin; Haoyue Wang; Rahul Mazumder (NeurIPS, 2024)

FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference. - Zirui Liu 001; Qingquan Song; Qiang Charles Xiao; Sathiya Keerthi Selvaraj; Rahul Mazumder; Aman Gupta; Xia Hu 001 (CoRR, 2024)

Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests. - Brian Liu 002; Rahul Mazumder (CoRR, 2024)

Efficient user history modeling with amortized inference for deep learning recommendation models. - Lars Hertel; Neil Daftary; Fedor Borisyuk; Aman Gupta; Rahul Mazumder (CoRR, 2024)

Sparse NMF with Archetypal Regularization: Computational and Robustness Properties. - Kayhan Behdin; Rahul Mazumder (J. Mach. Learn. Res., 2024)

Subgradient Regularized Multivariate Convex Regression at Scale. - Wenyu Chen 003; Rahul Mazumder (SIAM J. Optim., 2024)

PolyCD: Optimization via Cycling through the Vertices of a Polytope. - Rahul Mazumder; Haoyue Wang (SIAM J. Optim., 2024)

A new computational framework for log-concave density estimation. - Wenyu Chen 003; Rahul Mazumder; Richard J. Samworth (Math. Program. Comput., 2024)

A unified framework for Sparse plus Low-Rank Matrix Decomposition for LLMs. - Mehdi Makni; Kayhan Behdin; Zheng Xu 002; Natalia Ponomareva 001; Rahul Mazumder (CPAL, 2025)

Preserving Deep Representations in One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework. - Ryan Lucas; Rahul Mazumder (ICLR, 2025)

Efficient Algorithms for Leveraging LLMs for Generative and Predictive Recommender Systems. - Kayhan Behdin; Yun Dai; Gregory Dexter; Aman Gupta; Rahul Mazumder; Ankan Saha; Qingquan Song; Shao Tang; Sirou Zhu; Pin-Lun Hsu (WWW (Companion Volume), 2025)

Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach. - Haoyue Wang; Shibal Ibrahim; Rahul Mazumder (SIAM J. Math. Data Sci., 2025)

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs. - Mehdi Makni; Kayhan Behdin; Zheng Xu 002; Natalia Ponomareva 001; Rahul Mazumder (CoRR, 2025)

Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications. - Kayhan Behdin; Yun Dai; Ata Fatahi Baarzi; Aman Gupta; Qingquan Song; Shao Tang; Hejian Sang; Gregory Dexter; Sirou Zhu; Siyu Zhu; Tejas Dharamsi; Maziar Sanjabi; et al. (CoRR, 2025)

An Optimization Framework for Differentially Private Sparse Fine-Tuning. - Mehdi Makni; Kayhan Behdin; Gabriel Afriat; Zheng Xu 002; Sergei Vassilvitskii; Natalia Ponomareva 001; Hussein Hazimeh 001; Rahul Mazumder (CoRR, 2025)

TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks. - Xiang Meng; Mehdi Makni; Rahul Mazumder (CoRR, 2025)

MOSS: Multi-Objective Optimization for Stable Rule Sets. - Brian Liu 002; Rahul Mazumder (CoRR, 2025)

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives. - Brian Liu; Rahul Mazumder; Peter Radchenko (CoRR, 2025)