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20082026
most citedMatrix Completion and Low-Rank SVD via Fast Alternating Least Squares

307 citations · 693 across the 70 of their papers we have counts for

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Showing 2023 · stat.MLShow all

5 papers · 2 filters

stat.ML2023★ 1 cited

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…

stat.ML2023★ 1 cited

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…

stat.ML2023

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…

stat.ML2023★ 1 cited

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…

stat.ML2023★ 1 cited

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…