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ORCIDRahul Mazumder
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2020 – today
- 2024
[j23]Kayhan Behdin, Rahul Mazumder:
Sparse NMF with Archetypal Regularization: Computational and Robustness Properties. J. Mach. Learn. Res. 25: 36:1-36:62 (2024)- [j22]Wenyu Chen, Rahul Mazumder, Richard J. Samworth:
A new computational framework for log-concave density estimation. Math. Program. Comput. 16(2): 185-228 (2024) - [j21]Wenyu Chen
, Rahul Mazumder:
Subgradient Regularized Multivariate Convex Regression at Scale. SIAM J. Optim. 34(3): 2350-2377 (2024) - [j20]Rahul Mazumder, Haoyue Wang:
PolyCD: Optimization via Cycling through the Vertices of a Polytope. SIAM J. Optim. 34(4): 3534-3563 (2024) - [c27]Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder:
End-to-end Feature Selection Approach for Learning Skinny Trees. AISTATS 2024: 2863-2871 - [c26]Xiang Meng, Wenyu Chen, Riade Benbaki, Rahul Mazumder:
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning. AISTATS 2024: 4384-4392 - [c25]Xiang Meng, Shibal Ibrahim, Kayhan Behdin, Hussein Hazimeh, Natalia Ponomareva, Rahul Mazumder:
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization. ICML 2024 - [c24]Brian Liu, Rahul Mazumder:
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML. KDD 2024: 1863-1874 - [i39]Zirui Liu, Qingquan Song, Qiang Charles Xiao, Sathiya Keerthi Selvaraj, Rahul Mazumder, Aman Gupta, Xia Hu:
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference. CoRR abs/2401.04044 (2024) - [i38]Brian Liu, Rahul Mazumder:
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML. CoRR abs/2402.12630 (2024) - [i37]Brian Liu, Rahul Mazumder:
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests. CoRR abs/2402.12668 (2024) - [i36]Xiang Meng, Wenyu Chen, Riade Benbaki, Rahul Mazumder:
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning. CoRR abs/2403.07094 (2024) - [i35]Xiang Meng, Shibal Ibrahim, Kayhan Behdin, Hussein Hazimeh, Natalia Ponomareva, Rahul Mazumder:
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization. CoRR abs/2403.12983 (2024) - [i34]Xiang Meng, Kayhan Behdin, Haoyue Wang, Rahul Mazumder:
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models. CoRR abs/2406.07831 (2024) - 2023
- [j19]Rahul Mazumder, Peter Radchenko, Antoine Dedieu:
Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low. Oper. Res. 71(1): 129-147 (2023) - [j18]Hussein Hazimeh, Rahul Mazumder, Tim Nonet:
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization. J. Mach. Learn. Res. 24: 205:1-205:8 (2023) - [j17]Rahul Mazumder, Haoyue Wang:
Linear regression with partially mismatched data: local search with theoretical guarantees. Math. Program. 197(2): 1265-1303 (2023) - [c23]Brian Liu, Rahul Mazumder:
ForestPrune: Compact Depth-Pruned Tree Ensembles. AISTATS 2023: 9417-9428 - [c22]Ayan Acharya, Siyuan Gao, Ankan Saha, Borja Ocejo, Kinjal Basu, Sathiya Keerthi Selvaraj, Rahul Mazumder, Aman Gupta, Parag Agrawal:
Optimizing for Member Value in an Edge Building Marketplace. CIKM 2023: 5-14 - [c21]Shibal Ibrahim, Max Tell, Rahul Mazumder:
Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction. ICAIF 2023: 167-175 - [c20]Wenyu Chen, Riade Benbaki, Yada Zhu, Rahul Mazumder:
Dynamic Covariance Estimation under Structural Assumptions via a Joint Optimization Approach. ICAIF 2023: 445-453 - [c19]Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder:
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. ICML 2023: 2031-2049 - [c18]Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder:
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search. KDD 2023: 832-844 - [c17]Brian Liu, Rahul Mazumder:
Fire: An Optimization Approach for Fast Interpretable Rule Extraction. KDD 2023: 1396-1405 - [c16]Aman Gupta, S. Sathiya Keerthi, Ayan Acharya, Miao Cheng, Borja Ocejo Elizondo, Rohan Ramanath, Rahul Mazumder, Kinjal Basu, J. Kenneth Tay, Rupesh Gupta:
Practical Design of Performant Recommender Systems using Large-scale Linear Programming-based Global Inference. KDD 2023: 5781-5782 - [c15]Shibal Ibrahim, Gabriel Afriat, Kayhan Behdin, Rahul Mazumder:
GRAND-SLAMIN' Interpretable Additive Modeling with Structural Constraints. NeurIPS 2023 - [c14]Rahul Mazumder, Haoyue Wang:
On the Convergence of CART under Sufficient Impurity Decrease Condition. NeurIPS 2023 - [c13]Ayan Acharya, Siyuan Gao, Borja Ocejo, Kinjal Basu, Ankan Saha, Sathiya Keerthi Selvaraj, Rahul Mazumder, Parag Agrawal, Aman Gupta:
Promoting Inactive Members in Edge-Building Marketplace. WWW (Companion Volume) 2023: 945-949 - [i33]Kayhan Behdin, Qingquan Song, Aman Gupta, Ayan Acharya, David Durfee, Borja Ocejo, S. Sathiya Keerthi, Rahul Mazumder:
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization. CoRR abs/2302.09693 (2023) - [i32]Kayhan Behdin, Rahul Mazumder:
Sharpness-Aware Minimization: An Implicit Regularization Perspective. CoRR abs/2302.11836 (2023) - [i31]Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder:
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. CoRR abs/2302.14623 (2023) - [i30]Hanbyul Lee, Rahul Mazumder, Qifan Song, Jean Honorio:
Matrix Completion from General Deterministic Sampling Patterns. CoRR abs/2306.02283 (2023) - [i29]Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder:
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search. CoRR abs/2306.02824 (2023) - [i28]Brian Liu, Rahul Mazumder:
FIRE: An Optimization Approach for Fast Interpretable Rule Extraction. CoRR abs/2306.07432 (2023) - [i27]Kayhan Behdin, Wenyu Chen, Rahul Mazumder:
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives. CoRR abs/2307.09366 (2023) - [i26]Kayhan Behdin, Ayan Acharya, Aman Gupta, Sathiya Keerthi Selvaraj, Rahul Mazumder:
QuantEase: Optimization-based Quantization for Language Models - An Efficient and Intuitive Algorithm. CoRR abs/2309.01885 (2023) - [i25]Rahul Mazumder, Haoyue Wang:
On the Convergence of CART under Sufficient Impurity Decrease Condition. CoRR abs/2310.17114 (2023) - [i24]Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder:
End-to-end Feature Selection Approach for Learning Skinny Trees. CoRR abs/2310.18542 (2023) - 2022
- [j16]Santanu S. Dey, Rahul Mazumder, Guanyi Wang:
Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA. Oper. Res. 70(3): 1914-1932 (2022) - [j15]Antoine Dedieu, Rahul Mazumder, Haoyue Wang:
Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation. J. Mach. Learn. Res. 23: 164:1-164:41 (2022) - [j14]Hussein Hazimeh, Rahul Mazumder, Ali Saab:
Sparse regression at scale: branch-and-bound rooted in first-order optimization. Math. Program. 196(1): 347-388 (2022) - [j13]Haoyue Wang, Haihao Lu, Rahul Mazumder:
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning. SIAM J. Optim. 32(4): 2938-2968 (2022) - [c12]Shibal Ibrahim, Wenyu Chen, Yada Zhu, Pin-Yu Chen, Yang Zhang, Rahul Mazumder:
Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series. ICAIF 2022: 480-488 - [c11]Rahul Mazumder, Xiang Meng, Haoyue Wang:
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features. ICML 2022: 15255-15277 - [c10]Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder:
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles. KDD 2022: 666-675 - [c9]Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu:
Pushing the limits of fairness impossibility: Who's the fairest of them all? NeurIPS 2022 - [c8]Shibal Ibrahim, Natalia Ponomareva, Rahul Mazumder:
Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance. ECML/PKDD (1) 2022: 693-709 - [i23]Hussein Hazimeh, Rahul Mazumder, Tim Nonet:
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization. CoRR abs/2202.04820 (2022) - [i22]Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder:
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles. CoRR abs/2205.09717 (2022) - [i21]Brian Liu, Rahul Mazumder:
ForestPrune: Compact Depth-Controlled Tree Ensembles. CoRR abs/2206.00128 (2022) - [i20]Rahul Mazumder, Xiang Meng, Haoyue Wang:
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features. CoRR abs/2206.11844 (2022) - [i19]Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu:
Pushing the limits of fairness impossibility: Who's the fairest of them all? CoRR abs/2208.12606 (2022) - [i18]Kayhan Behdin, Qingquan Song, Aman Gupta, David Durfee, Ayan Acharya, S. Sathiya Keerthi, Rahul Mazumder:
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization. CoRR abs/2212.04343 (2022) - 2021
- [j12]Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder:
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives. J. Mach. Learn. Res. 22: 135:1-135:47 (2021) - [c7]Rahul Mazumder, Haoyue Wang:
Linear Regression with Mismatched Data: A Provably Optimal Local Search Algorithm. IPCO 2021: 443-457 - [c6]Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed H. Chi:
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. NeurIPS 2021: 29335-29347 - [i17]Kayhan Behdin, Rahul Mazumder:
Archetypal Analysis for Sparse Nonnegative Matrix Factorization: Robustness Under Misspecification. CoRR abs/2104.03527 (2021) - [i16]Hussein Hazimeh, Rahul Mazumder, Peter Radchenko:
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives. CoRR abs/2104.07084 (2021) - [i15]Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed H. Chi:
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. CoRR abs/2106.03760 (2021) - [i14]Shibal Ibrahim, Rahul Mazumder, Peter Radchenko, Emanuel Ben-David:
Predicting Census Survey Response Rates via Interpretable Nonparametric Additive Models with Structured Interactions. CoRR abs/2108.11328 (2021) - [i13]Gabriel Loewinger, Rolando Acosta Nunez, Rahul Mazumder, Giovanni Parmigiani:
Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation. CoRR abs/2109.09164 (2021) - [i12]Shibal Ibrahim, Natalia Ponomareva, Rahul Mazumder:
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance. CoRR abs/2110.06893 (2021) - 2020
- [j11]Hussein Hazimeh, Rahul Mazumder:
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms. Oper. Res. 68(5): 1517-1537 (2020) - [j10]Rahul Mazumder, Diego Saldana, Haolei Weng:
Matrix completion with nonconvex regularization: spectral operators and scalable algorithms. Stat. Comput. 30(4): 1113-1138 (2020) - [j9]Haihao Lu, Rahul Mazumder:
Randomized Gradient Boosting Machine. SIAM J. Optim. 30(4): 2780-2808 (2020) - [c5]Hussein Hazimeh, Rahul Mazumder:
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach. AISTATS 2020: 1833-1843 - [c4]Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan:
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications. ICML 2020: 704-714 - [c3]Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder:
The Tree Ensemble Layer: Differentiability meets Conditional Computation. ICML 2020: 4138-4148 - [i11]Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder:
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives. CoRR abs/2001.06471 (2020) - [i10]Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder:
The Tree Ensemble Layer: Differentiability meets Conditional Computation. CoRR abs/2002.07772 (2020) - [i9]Hussein Hazimeh, Rahul Mazumder, Ali Saab:
Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization. CoRR abs/2004.06152 (2020)
2010 – 2019
- 2019
- [j8]Hari Bandi, Dimitris Bertsimas, Rahul Mazumder:
Learning a Mixture of Gaussians via Mixed-Integer Optimization. INFORMS J. Optim. 1(3): 221-240 (2019) - [j7]Koulik Khamaru, Rahul Mazumder:
Computation of the maximum likelihood estimator in low-rank factor analysis. Math. Program. 176(1-2): 279-310 (2019) - [i8]Antoine Dedieu, Rahul Mazumder:
Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation. CoRR abs/1901.01585 (2019) - [i7]Hussein Hazimeh, Rahul Mazumder:
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach. CoRR abs/1902.01542 (2019) - 2018
- [c2]Antoine Dedieu, Rahul Mazumder, Zhen Zhu, Hossein Vahabi:
Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming. CIKM 2018: 607-616 - [i6]Antoine Dedieu, Rahul Mazumder, Zhen Zhu, Hossein Vahabi:
Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming. CoRR abs/1803.01440 (2018) - [i5]Robert M. Freund, Paul Grigas, Rahul Mazumder:
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods. CoRR abs/1810.08727 (2018) - [i4]Haihao Lu, Rahul Mazumder:
Randomized Gradient Boosting Machine. CoRR abs/1810.10158 (2018) - 2017
- [j6]Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder:
Certifiably Optimal Low Rank Factor Analysis. J. Mach. Learn. Res. 18: 29:1-29:53 (2017) - [j5]Robert M. Freund, Paul Grigas, Rahul Mazumder:
An Extended Frank-Wolfe Method with "In-Face" Directions, and Its Application to Low-Rank Matrix Completion. SIAM J. Optim. 27(1): 319-346 (2017) - [j4]Rahul Mazumder, Peter Radchenko:
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization. IEEE Trans. Inf. Theory 63(5): 3053-3075 (2017) - 2015
- [j3]Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh:
Matrix completion and low-rank SVD via fast alternating least squares. J. Mach. Learn. Res. 16: 3367-3402 (2015) - [i3]Robert M. Freund, Paul Grigas, Rahul Mazumder:
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives. CoRR abs/1505.04243 (2015) - 2013
- [c1]Dennis L. Sun, Rahul Mazumder:
Non-negative matrix completion for bandwidth extension: A convex optimization approach. MLSP 2013: 1-6 - [i2]Robert M. Freund, Paul Grigas, Rahul Mazumder:
AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods. CoRR abs/1307.1192 (2013) - 2012
- [j2]Rahul Mazumder, Trevor Hastie:
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. J. Mach. Learn. Res. 13: 781-794 (2012) - 2011
- [i1]Rahul Mazumder, Trevor Hastie:
The Graphical Lasso: New Insights and Alternatives. CoRR abs/1111.5479 (2011) - 2010
- [j1]Rahul Mazumder, Trevor Hastie, Robert Tibshirani:
Spectral Regularization Algorithms for Learning Large Incomplete Matrices. J. Mach. Learn. Res. 11: 2287-2322 (2010)
Coauthor Index
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last updated on 2024-12-03 20:29 CET by the dblp team
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