307 citations · 693 across the 70 of their papers we have counts for
21 papers · 1 filter
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
Brian Liu, Rahul Mazumder
We study the often overlooked phenomenon, first noted in \cite{breiman2001random}, that random forests appear to reduce bias compared to bagging. Motivated by an interesting paper…
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
Brian Liu, Rahul Mazumder
We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent add…
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…
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…
Matrix Completion from General Deterministic Sampling Patterns
Hanbyul Lee, Rahul Mazumder, Qifan Song +1
Most of the existing works on provable guarantees for low-rank matrix completion algorithms rely on some unrealistic assumptions such that matrix entries are sampled randomly or th…
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…