4 citations · 17 across the 25 of their papers we have counts for
5 papers · 1 filter
End-to-end Feature Selection Approach for Learning Skinny Trees
Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder
We propose a new optimization-based approach for feature selection in tree ensembles, an important problem in statistics and machine learning. Popular tree ensemble toolkits e.g.,…
QuantEase: Optimization-based Quantization for Language Models
Kayhan Behdin, Ayan Acharya, Aman Gupta +4
With the rising popularity of Large Language Models (LLMs), there has been an increasing interest in compression techniques that enable their efficient deployment. This study focus…
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
Kayhan Behdin, Wenyu Chen, Rahul Mazumder
We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given samples from a multiv…
On Statistical Properties of Sharpness-Aware Minimization: Provable Guarantees
Kayhan Behdin, Rahul Mazumder
Sharpness-Aware Minimization (SAM) is a recent optimization framework aiming to improve the deep neural network generalization, through obtaining flatter (i.e. less sharp) solution…
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +7
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…