| Improved deep neural network generalization using m-sharpness-aware minimization K Behdin, Q Song, A Gupta, D Durfee, A Acharya, S Keerthi, R Mazumder arXiv preprint arXiv:2212.04343, 2022 | 17* | 2022 |
| Quantease: Optimization-based quantization for language models K Behdin, A Acharya, A Gupta, Q Song, S Zhu, S Keerthi, R Mazumder arXiv preprint arXiv:2309.01885, 2023 | 15* | 2023 |
| Transductive multi-label learning from missing data using smoothed rank function A Esmaeili, K Behdin, MA Fakharian, F Marvasti Pattern Analysis and Applications 23 (3), 1225-1233, 2020 | 15* | 2020 |
| OBTAIN: Real-Time Beat Tracking in Audio Signals A Mottaghi, K Behdin, A Esmaeili, M Heydari, F Marvasti ICOSP 2017, The workshop of ICCSIT 2017, Florence, Italy, 2017 | 15 | 2017 |
| Missing low-rank and sparse decomposition based on smoothed nuclear norm M Azghani, A Esmaeili, K Behdin, F Marvasti IEEE Transactions on Circuits and Systems for Video Technology 30 (6), 1550-1558, 2019 | 13 | 2019 |
| On Statistical Properties of Sharpness-Aware Minimization: Provable Guarantees K Behdin, R Mazumder arXiv preprint arXiv:2302.11836, 2023 | 12* | 2023 |
| Sparse PCA: A new scalable estimator based on integer programming K Behdin, R Mazumder arXiv preprint arXiv:2109.11142, 2021 | 9 | 2021 |
| 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 | 8 | 2024 |
| Alps: Improved optimization for highly sparse one-shot pruning for large language models X Meng, K Behdin, H Wang, R Mazumder arXiv preprint arXiv:2406.07831, 2024 | 7 | 2024 |
| GRAND-SLAMIN’Interpretable Additive Modeling with Structural Constraints S Ibrahim, G Afriat, K Behdin, R Mazumder Advances in Neural Information Processing Systems 36, 61158-61186, 2023 | 4 | 2023 |
| Sparse gaussian graphical models with discrete optimization: Computational and statistical perspectives K Behdin, W Chen, R Mazumder arXiv preprint arXiv:2307.09366, 2023 | 4 | 2023 |
| Sparse NMF with Archetypal Regularization: Computational and Robustness Properties K Behdin, R Mazumder Journal of Machine Learning Research 25 (36), 1-62, 2024 | 3* | 2024 |
| Recovering quantized data with missing information using bilinear factorization and augmented Lagrangian method A Esmaeili, K Behdin, F Marvasti arXiv preprint arXiv:1810.03222, 2018 | 3 | 2018 |
| End-to-end feature selection approach for learning skinny trees S Ibrahim, K Behdin, R Mazumder International Conference on Artificial Intelligence and Statistics, 2863-2871, 2024 | 1 | 2024 |
| Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications K Behdin, Y Dai, A Fatahibaarzi, A Gupta, Q Song, S Tang, H Sang, ... arXiv preprint arXiv:2502.14305, 2025 | | 2025 |
| HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs M Makni, K Behdin, Z Xu, N Ponomareva, R Mazumder arXiv preprint arXiv:2502.00899, 2025 | | 2025 |
| Differentially Private Best Subset Selection Via Integer Programming K Behdin, P Prastakos, R Mazumder Privacy Regulation and Protection in Machine Learning, 2024 | | 2024 |
| Statistical Learning with Discrete Structures: Statistical and Computational Perspectives K Behdin Massachusetts Institute of Technology, 2024 | | 2024 |
| Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives K Behdin, G Loewinger, KT Kishida, G Parmigiani, R Mazumder arXiv preprint arXiv:2212.08697, 2022 | | 2022 |
| Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach G Loewinger, K Behdin, KT Kishida, G Parmigiani, R Mazumder arXiv preprint arXiv:2212.08697, 2022 | | 2022 |
