{"id":17893795,"url":"https://github.com/shreypandit/quantile_regression-using_lalr","last_synced_at":"2025-08-01T07:13:18.774Z","repository":{"id":111359240,"uuid":"293768701","full_name":"ShreyPandit/Quantile_Regression-Using_LALR","owner":"ShreyPandit","description":"Quantile Regression using check loss under the influence of Lipschitz Adaptive Learning Rate","archived":false,"fork":false,"pushed_at":"2020-11-06T09:33:10.000Z","size":1216,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-03T04:17:22.786Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ShreyPandit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-09-08T09:51:14.000Z","updated_at":"2023-03-10T08:26:10.000Z","dependencies_parsed_at":null,"dependency_job_id":"1734b5d0-e57a-48d9-8ff4-a3bbcd510494","html_url":"https://github.com/ShreyPandit/Quantile_Regression-Using_LALR","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ShreyPandit/Quantile_Regression-Using_LALR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FQuantile_Regression-Using_LALR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FQuantile_Regression-Using_LALR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FQuantile_Regression-Using_LALR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FQuantile_Regression-Using_LALR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ShreyPandit","download_url":"https://codeload.github.com/ShreyPandit/Quantile_Regression-Using_LALR/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FQuantile_Regression-Using_LALR/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268185199,"owners_count":24209381,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-01T02:00:08.611Z","response_time":67,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-28T14:56:07.698Z","updated_at":"2025-08-01T07:13:18.752Z","avatar_url":"https://github.com/ShreyPandit.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Quantile-Regression_Lipschitz\nImplemented the Paper - LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks\u003cbr\u003e\nThe purpose was using Quantile Regression with check loss under the influence of Lipschitz Adaptive Learning Rate \u003cbr\u003e\n# Dataset \u003cbr\u003e\nThere were 3 dataset used \u003cbr\u003e\n1) California Housing Dataset \u003cbr\u003e\n2) Boston Housing Dataset \u003cbr\u003e\n3) Energy Efficiency Dataset \u003cbr\u003e\n# Result \u003cbr\u003e\nThe result showed the performance was better while using LALR as the learning Rate in all 3 dataset as compared to constant LR \u003cbr\u003e\nCalifornia Housing\u003cbr\u003e\n\u003cimg src=\"./Images/cali.png\" alt=\"result image\"/\u003e \u003cbr\u003e\nBoston Housing \u003cbr\u003e\n\u003cimg src=\"./Images/boston.png\" alt=\"result image\"/\u003e \u003cbr\u003e\nEnergy Efficiency \u003cbr\u003e\n\u003cimg src=\"./Images/energy.png\" alt=\"result image\"/\u003e \u003cbr\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreypandit%2Fquantile_regression-using_lalr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshreypandit%2Fquantile_regression-using_lalr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreypandit%2Fquantile_regression-using_lalr/lists"}