{"id":19529286,"url":"https://github.com/isl-org/generalized-smoothing","last_synced_at":"2025-06-19T16:41:06.479Z","repository":{"id":103594137,"uuid":"566113131","full_name":"isl-org/generalized-smoothing","owner":"isl-org","description":"Companion code for the ICML 2022 paper \"Generalizing Gaussian Smoothing for Random Search\"","archived":false,"fork":false,"pushed_at":"2024-07-22T05:28:15.000Z","size":14,"stargazers_count":8,"open_issues_count":0,"forks_count":1,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-17T18:21:39.139Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/isl-org.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2022-11-15T01:42:59.000Z","updated_at":"2024-12-30T08:41:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"b3cd8178-a9d0-49e7-aff7-e8fc218d494b","html_url":"https://github.com/isl-org/generalized-smoothing","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/isl-org/generalized-smoothing","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2Fgeneralized-smoothing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2Fgeneralized-smoothing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2Fgeneralized-smoothing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2Fgeneralized-smoothing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/isl-org","download_url":"https://codeload.github.com/isl-org/generalized-smoothing/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2Fgeneralized-smoothing/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260789716,"owners_count":23063620,"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","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":"2024-11-11T01:23:23.696Z","updated_at":"2025-06-19T16:41:01.469Z","avatar_url":"https://github.com/isl-org.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Generalizing Gaussian Smoothing for Random Search\n\nThis repository contains code implementing the algorithms proposed in the paper [Generalizing Gaussian Smoothing for Random Search](https://proceedings.mlr.press/v162/gao22f.html), Gao and Sener (ICML 2022).\n\nIn particular, we provide the code used to obtain the experimental results on linear regression and the Nevergrad benchmark.\nFor online RL, we used the [ARS](https://github.com/modestyachts/ARS) repository; our proposed algorithms may be implemented by modifying the sampling distribution of the shared noise table.\nPlease see the paper for additional details and the hyperparameters used.\n\n## Requirements\n\nThe code is written in Python 3.\nAside from the standard libraries, [NumPy](https://numpy.org/) and [Matplotlib](https://matplotlib.org/) are needed.\nFor linear regression, you also need [SciPy](https://scipy.org/), and for Nevergrad the corresponding [package](https://facebookresearch.github.io/nevergrad/).\n\n## Running the experiments\n\nPlease see the READMEs in the `LinearRegression` and `benchmarks` folders for further instructions.\n\n## Citation\n\nTo cite this repository in your research, please reference the following [paper]():\n\n\u003e Gao, Katelyn, and Ozan Sener. \"Generalizing Gaussian Smoothing for Random Search.\" International Conference on Machine Learning. PMLR, 2022.\n\n```TeX\n@inproceedings{gao2022generalizing,\n  title={Generalizing Gaussian Smoothing for Random Search},\n  author={Gao, Katelyn and Sener, Ozan},\n  booktitle={International Conference on Machine Learning},\n  pages={7077--7101},\n  year={2022},\n  organization={PMLR}\n}\n```\n\n## Contact\n\nIf you have questions, please contact \u003ckatelyn.gao@intel.com\u003e.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisl-org%2Fgeneralized-smoothing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fisl-org%2Fgeneralized-smoothing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisl-org%2Fgeneralized-smoothing/lists"}