307 citations · 693 across the 70 of their papers we have counts for
12 papers · 1 filter
On the Convergence of CART under Sufficient Impurity Decrease Condition
Rahul Mazumder, Haoyue Wang
The decision tree is a flexible machine learning model that finds its success in numerous applications. It is usually fitted in a recursively greedy manner using CART. In this pape…
Linear programming using diagonal linear networks
Haoyue Wang, Promit Ghosal, Rahul Mazumder
Linear programming has played a crucial role in shaping decision-making, resource allocation, and cost reduction in various domains. In this paper, we investigate the application o…
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…
FIRE: An Optimization Approach for Fast Interpretable Rule Extraction
Brian Liu, Rahul Mazumder
We present FIRE, Fast Interpretable Rule Extraction, an optimization-based framework to extract a small but useful collection of decision rules from tree ensembles. FIRE selects sp…