4 citations · 17 across the 25 of their papers we have counts for
7 papers · 1 filter
Scaling Up Efficient Small Language Models Serving and Deployment for Semantic Job Search
Kayhan Behdin, Qingquan Song, Sriram Vasudevan +17
Large Language Models (LLMs) have demonstrated impressive quality when applied to predictive tasks such as relevance ranking and semantic search. However, deployment of such LLMs r…
Differentially Private High-dimensional Variable Selection via Integer Programming
Petros Prastakos, Kayhan Behdin, Rahul Mazumder
Sparse variable selection improves interpretability and generalization in high-dimensional learning by selecting a small subset of informative features. Recent advances in Mixed In…
Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction
Ryan Lucas, Kayhan Behdin, Zhipeng Wang +3
Reasoning language models such as DeepSeek-R1 produce long chain-of-thought traces during inference time which make them costly to deploy at scale. We show that using compression t…
BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation
Fengyi Li, Kayhan Behdin, Natesh Pillai +3
Text segmentation based on the semantic meaning of sentences is a fundamental task with broad utility in many downstream applications. In this paper, we propose a graphical model-b…
An Optimization Framework for Differentially Private Sparse Fine-Tuning
Mehdi Makni, Kayhan Behdin, Gabriel Afriat +5
Differentially private stochastic gradient descent (DP-SGD) is broadly considered to be the gold standard for training and fine-tuning neural networks under differential privacy (D…
HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs
Mehdi Makni, Kayhan Behdin, Zheng Xu +2
The impressive capabilities of large foundation models come at a cost of substantial computing resources to serve them. Compressing these pre-trained models is of practical interes…
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song +17
Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Al…