Rahul Mazumder
Orcid: 0000-0003-1384-9743
According to our database1,
Rahul Mazumder
authored at least 69 papers
between 2010 and 2024.
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Bibliography
2024
Math. Program. Comput., June, 2024
SIAM J. Optim., 2024
J. Mach. Learn. Res., 2024
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models.
CoRR, 2024
CoRR, 2024
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference.
CoRR, 2024
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024
2023
Linear regression with partially mismatched data: local search with theoretical guarantees.
Math. Program., February, 2023
Oper. Res., January, 2023
J. Mach. Learn. Res., 2023
QuantEase: Optimization-based Quantization for Language Models - An Efficient and Intuitive Algorithm.
CoRR, 2023
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives.
CoRR, 2023
Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
Practical Design of Performant Recommender Systems using Large-scale Linear Programming-based Global Inference.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
Proceedings of the International Conference on Machine Learning, 2023
Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction.
Proceedings of the 4th ACM International Conference on AI in Finance, 2023
Dynamic Covariance Estimation under Structural Assumptions via a Joint Optimization Approach.
Proceedings of the 4th ACM International Conference on AI in Finance, 2023
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning.
SIAM J. Optim., December, 2022
Math. Program., 2022
Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation.
J. Mach. Learn. Res., 2022
Oper. Res., 2022
CoRR, 2022
Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features.
Proceedings of the International Conference on Machine Learning, 2022
Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series.
Proceedings of the 3rd ACM International Conference on AI in Finance, 2022
2021
J. Mach. Learn. Res., 2021
Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation.
CoRR, 2021
Predicting Census Survey Response Rates via Interpretable Nonparametric Additive Models with Structured Interactions.
CoRR, 2021
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives.
CoRR, 2021
Archetypal Analysis for Sparse Nonnegative Matrix Factorization: Robustness Under Misspecification.
CoRR, 2021
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Integer Programming and Combinatorial Optimization, 2021
2020
Matrix completion with nonconvex regularization: spectral operators and scalable algorithms.
Stat. Comput., 2020
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms.
Oper. Res., 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
INFORMS J. Optim., July, 2019
Math. Program., 2019
Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation.
CoRR, 2019
2018
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods.
CoRR, 2018
Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming.
CoRR, 2018
Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming.
Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018
2017
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization.
IEEE Trans. Inf. Theory, 2017
An Extended Frank-Wolfe Method with "In-Face" Directions, and Its Application to Low-Rank Matrix Completion.
SIAM J. Optim., 2017
2015
J. Mach. Learn. Res., 2015
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives.
CoRR, 2015
2013
AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods.
CoRR, 2013
Non-negative matrix completion for bandwidth extension: A convex optimization approach.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013
2012
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso.
J. Mach. Learn. Res., 2012
2011
2010
J. Mach. Learn. Res., 2010