| Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms H Hazimeh, R Mazumder Operations Research 68 (5), 1517-1537, 2020 | 209 | 2020 |
| How to dp-fy ml: A practical guide to machine learning with differential privacy N Ponomareva, H Hazimeh, A Kurakin, Z Xu, C Denison, HB McMahan, ... Journal of Artificial Intelligence Research 77, 1113-1201, 2023 | 135 | 2023 |
| Dselect-k: Differentiable selection in the mixture of experts with applications to multi-task learning H Hazimeh, Z Zhao, A Chowdhery, M Sathiamoorthy, Y Chen, ... Advances in Neural Information Processing Systems 34, 2021 | 128 | 2021 |
| The tree ensemble layer: Differentiability meets conditional computation H Hazimeh, N Ponomareva, P Mol, Z Tan, R Mazumder International Conference on Machine Learning, 4138-4148, 2020 | 108 | 2020 |
| Sparse regression at scale: Branch-and-bound rooted in first-order optimization H Hazimeh, R Mazumder, A Saab Mathematical Programming 196 (1-2), 347-388, 2022 | 102 | 2022 |
| Learning sparse classifiers: Continuous and mixed integer optimization perspectives A Dedieu, H Hazimeh, R Mazumder Journal of Machine Learning Research 22 (135), 1-47, 2021 | 57 | 2021 |
| Grouped variable selection with discrete optimization: Computational and statistical perspectives H Hazimeh, R Mazumder, P Radchenko The Annals of Statistics 51 (1), 1-32, 2023 | 31 | 2023 |
| Axiomatic analysis of smoothing methods in language models for pseudo-relevance feedback H Hazimeh, CX Zhai Proceedings of the 2015 international conference on the theory of …, 2015 | 30 | 2015 |
| Learning hierarchical interactions at scale: A convex optimization approach H Hazimeh, R Mazumder International Conference on Artificial Intelligence and Statistics, 1833-1843, 2020 | 25 | 2020 |
| Scaling laws for downstream task performance of large language models B Isik, N Ponomareva, H Hazimeh, D Paparas, S Vassilvitskii, S Koyejo arXiv preprint arXiv:2402.04177, 2024 | 21 | 2024 |
| Fast as chita: Neural network pruning with combinatorial optimization R Benbaki, W Chen, X Meng, H Hazimeh, N Ponomareva, Z Zhao, ... International Conference on Machine Learning, 2031-2049, 2023 | 18 | 2023 |
| L0learn: A scalable package for sparse learning using l0 regularization H Hazimeh, R Mazumder, T Nonet Journal of Machine Learning Research 24 (205), 1-8, 2023 | 17* | 2023 |
| Benchmarking robustness to adversarial image obfuscations F Stimberg, A Chakrabarti, CT Lu, H Hazimeh, O Stretcu, W Qiao, Y Liu, ... Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles S Ibrahim, H Hazimeh, R Mazumder Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 5 | 2022 |
| OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization X Meng, S Ibrahim, K Behdin, H Hazimeh, N Ponomareva, R Mazumder arXiv preprint arXiv:2403.12983, 2024 | 4 | 2024 |
| Comet: Learning cardinality constrained mixture of experts with trees and local search S Ibrahim, W Chen, H Hazimeh, N Ponomareva, Z Zhao, R Mazumder Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 4 | 2023 |
| Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets H Hazimeh, N Ponomareva International Conference on Artificial Intelligence and Statistics, 3018-3033, 2023 | 2 | 2023 |
| DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation Y Wang, H Hazimeh, N Ponomareva, A Kurakin, I Hammoud, R Arora arXiv preprint arXiv:2402.11120, 2024 | 1 | 2024 |
| How to dp-fy ml: A practical guide to machine learning with differential privacy N Ponomareva, H Hazimeh, A Kurakin, Z Xu, C Denison, HB McMahan, ... arXiv preprint arXiv:2303.00654, 2023 | | 2023 |
| Sparse and differentiable mixture of experts neural networks Z Zhao, M Sathiamoorthy, L Hong, Y Chen, EHH Chi, A Chowdhery, ... US Patent App. 17/665,279, 2022 | | 2022 |
Rahul MazumderMassachusetts Institute of TechnologyBestätigte E-Mail-Adresse bei mit.edu
