Kayhan Behdin

Preprints

1. K. Behdin, R. Benbaki, P. Radchenko and R. Mazumder "Modeling with Categorical Features via Exact Fusion and Sparsity Regularisation", Major Revision, The Journal of the Royal Statistical Society, Series B (major revision).

2. K. Behdin, G. Loewinger, K. T. Kishida, G. Parmigiani and R. Mazumder "Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives ", The Journal of Royal Statistical Society, Series B (minor revision). [arxiv], [CRAN], [R package]

3. K. Behdin, W. Chen and R. Mazumder "Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives ", Operations Research (minor revision). [arxiv], [code]

Publications (2021-)

1. K. Behdin and R. Mazumder "Sparse PCA: A New Scalable Estimator Based On Integer Programming", Annals of Statistics (to appear), 2025. [arxiv], [code]

2. K. Behdin et al. “Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems”, EMNLP 2025 Industry Track (Oral, to appear). [code]

3. P. Prastakos, K. Behdin and R. Mazumder "Differentially Private High-dimensional Variable Selection via Integer Programming", NeurIPS, 2025 (to appear).

4. R. Lucas, K. Behdin, Z. Wang, Q. Song, S. Tang and R. Mazumder "Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction", NeurIPS 2025 Workshop on Efficient Reasoning (to appear). [arxiv]

5. M. Makni, K. Behdin, G. Afriat, Z. Xu, S. Vassilvitskii, N. Ponomareva, R. Mazumder and H. Hazimeh "SPARTA: An Optimization Framework for Differentially Private Sparse Fine-Tuning", KDD, 2025. [paper]

6. K. Behdin et al. "Efficient Algorithms for Leveraging LLMs for Generative and Predictive Recommender Systems", WWW, 2025 (Tutorial). [paper], [website]

7. M. Makni, K. Behdin, Z. Xu, N. Ponomareva and R. Mazumder "A Unified Framework for Sparse Plus Low-Rank Matrix Decomposition for LLMs", CPAL, 2025. [paper]

8. X. Meng, K. Behdin, H. Wang and R. Mazumder "ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models", NeurIPS, 2024. [paper]

9. X. Meng, S. Ibrahim, K. Behdin, H. Hazimeh, N. Ponomareva and R. Mazumder "OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization", ICML 2024. [paper]

10. K. Behdin and R. Mazumder "Sparse NMF with Archetypal Regularization: Computational and Robustness Properties", Journal of Machine Learning Research, 2024. [paper], [code]

11. S. Ibrahim, K. Behdin and R. Mazumder "End-to-end Feature Selection Approach for Learning Skinny Trees", AISTATS 2024 (Outstanding Student Paper Highlight). [paper]

12. S. Ibrahim, G. Afriat, K. Behdin and R. Mazumder "GRAND-SLAMIN’ Interpretable Additive Modeling with Structural Constraints", NeurIPS 2023. [paper]

13. K. Behdin, Q. Song, A. Gupta, D. Durfee, A. Acharya, S. Keerthi, R. Mazumder "Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization", NeurIPS OPT Workshop, 2022. [paper]

Publications (-2020)

1. A. Esmaeili, K. Behdin, M. A. Fakharian and F. Marvasti "Transductive Multi-label Learning From Missing Data Using Smoothed Rank Function", Pattern Analysis and Applications, 2020. [paper]

2. M. Azghani, A. Esmaeili, K. Behdin and F. Marvasti "Missing Low-Rank and Sparse Decomposition Based on Smoothed Nuclear Norm", IEEE Transactions on Circuits and Systems for Video Technology, 2019. [paper]