https://github.com/shreypandit/quantile_regression-using_lalr

Quantile Regression using check loss under the influence of Lipschitz Adaptive Learning Rate
https://github.com/shreypandit/quantile_regression-using_lalr

Last synced: 11 months ago
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Quantile Regression using check loss under the influence of Lipschitz Adaptive Learning Rate

• Host: GitHub
• URL: https://github.com/shreypandit/quantile_regression-using_lalr
• Owner: ShreyPandit
• Created: 2020-09-08T09:51:14.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-11-06T09:33:10.000Z (over 5 years ago)
• Last Synced: 2025-04-03T04:17:22.786Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 1.16 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Quantile-Regression_Lipschitz

Implemented the Paper - LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks


The purpose was using Quantile Regression with check loss under the influence of Lipschitz Adaptive Learning Rate 


# Dataset 


There were 3 dataset used 


1) California Housing Dataset 


2) Boston Housing Dataset 


3) Energy Efficiency Dataset 


# Result 


The result showed the performance was better while using LALR as the learning Rate in all 3 dataset as compared to constant LR 


California Housing


 


Boston Housing 


 


Energy Efficiency