{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generalizing-gaussian-smoothing-for-random","title":"Generalizing Gaussian Smoothing for Random Search","arxiv_id":"2211.14721","date":"2022-11-27","proceeding":null,"authors":["Katelyn Gao","Ozan Sener"],"abstract":"Gaussian smoothing (GS) is a derivative-free optimization (DFO) algorithm that estimates the gradient of an objective using perturbations of the current parameters sampled from a standard normal distribution. We generalize it to sampling perturbations from a larger family of distributions. Based on an analysis of DFO for non-convex functions, we propose to choose a distribution for perturbations that minimizes the mean squared error (MSE) of the gradient estimate. We derive three such distributions with provably smaller MSE than Gaussian smoothing. We conduct evaluations of the three sampling distributions on linear regression, reinforcement learning, and DFO benchmarks in order to validate our claims. Our proposal improves on GS with the same computational complexity, and are usually competitive with and often outperform Guided ES and Orthogonal ES, two computationally more expensive algorithms that adapt the covariance matrix of normally distributed perturbations.","url_abs":"https://arxiv.org/abs/2211.14721v1","url_pdf":"https://arxiv.org/pdf/2211.14721v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generalizing-gaussian-smoothing-for-random","repo_url":"https://github.com/isl-org/generalized-smoothing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.14721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.14721"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/isl-org/generalized-smoothing","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"9a08b9c25cf3ff73","entry":"generate_data","repo":"isl-org/generalized-smoothing","repo_kind":"official","path":"LinearRegression/samplers.py","file_url":"https://github.com/isl-org/generalized-smoothing/blob/HEAD/LinearRegression/samplers.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9a08b9c25cf3ff73"}},{"code_sha256_prefix":"451b9f211bc6b497","entry":"generate_task_random","repo":"isl-org/generalized-smoothing","repo_kind":"official","path":"LinearRegression/samplers.py","file_url":"https://github.com/isl-org/generalized-smoothing/blob/HEAD/LinearRegression/samplers.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"451b9f211bc6b497"}},{"code_sha256_prefix":"91cf78f6295fe25b","entry":"gram_schmidt","repo":"isl-org/generalized-smoothing","repo_kind":"official","path":"benchmarks/utils.py","file_url":"https://github.com/isl-org/generalized-smoothing/blob/HEAD/benchmarks/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"91cf78f6295fe25b"}},{"code_sha256_prefix":"676d98ceb3dd51b5","entry":"true_erm_grad","repo":"isl-org/generalized-smoothing","repo_kind":"official","path":"LinearRegression/samplers.py","file_url":"https://github.com/isl-org/generalized-smoothing/blob/HEAD/LinearRegression/samplers.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"676d98ceb3dd51b5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}