{"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/missing-data-imputation-and-acquisition-with","title":"Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo","arxiv_id":"2202.04599","date":"2022-02-09","proceeding":null,"authors":["Ignacio Peis","Chao Ma","José Miguel Hernández-Lobato"],"abstract":"Variational Autoencoders (VAEs) have recently been highly successful at imputing and acquiring heterogeneous missing data. However, within this specific application domain, existing VAE methods are restricted by using only one layer of latent variables and strictly Gaussian posterior approximations. To address these limitations, we present HH-VAEM, a Hierarchical VAE model for mixed-type incomplete data that uses Hamiltonian Monte Carlo with automatic hyper-parameter tuning for improved approximate inference. Our experiments show that HH-VAEM outperforms existing baselines in the tasks of missing data imputation and supervised learning with missing features. Finally, we also present a sampling-based approach for efficiently computing the information gain when missing features are to be acquired with HH-VAEM. Our experiments show that this sampling-based approach is superior to alternatives based on Gaussian approximations.","url_abs":"https://arxiv.org/abs/2202.04599v5","url_pdf":"https://arxiv.org/pdf/2202.04599v5.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":"missing-data-imputation-and-acquisition-with","repo_url":"https://github.com/ipeis/HH-VAEM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"imputation","task_name":"Imputation"}],"methods":[{"method_slug":"hierarchical-vae","method_name":"Hierarchical VAE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.04599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.04599"}},"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":"deterministic:regex_extraction","url":"https://github.com/ipeis/HH-VAEM","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":6},"by_repo_kind":{"official":{"samples":6,"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":"896634a7bacb2c3e","entry":"clean_dataset","repo":"ipeis/HH-VAEM","repo_kind":"official","path":"src/datasets.py","file_url":"https://github.com/ipeis/HH-VAEM/blob/HEAD/src/datasets.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":"896634a7bacb2c3e"}},{"code_sha256_prefix":"b2e25fb00193df4f","entry":"find_path","repo":"ipeis/HH-VAEM","repo_kind":"official","path":"src/load_models.py","file_url":"https://github.com/ipeis/HH-VAEM/blob/HEAD/src/load_models.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":"b2e25fb00193df4f"}},{"code_sha256_prefix":"f3ffb87ed2c67402","entry":"get_config","repo":"ipeis/HH-VAEM","repo_kind":"official","path":"src/configs.py","file_url":"https://github.com/ipeis/HH-VAEM/blob/HEAD/src/configs.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":"f3ffb87ed2c67402"}},{"code_sha256_prefix":"45cfc28509964fa4","entry":"mutual_information","repo":"ipeis/HH-VAEM","repo_kind":"official","path":"src/mutual_information.py","file_url":"https://github.com/ipeis/HH-VAEM/blob/HEAD/src/mutual_information.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":"45cfc28509964fa4"}},{"code_sha256_prefix":"6ad9b288c102f26a","entry":"quantize","repo":"ipeis/HH-VAEM","repo_kind":"official","path":"src/mutual_information.py","file_url":"https://github.com/ipeis/HH-VAEM/blob/HEAD/src/mutual_information.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":"6ad9b288c102f26a"}},{"code_sha256_prefix":"c154239dbec460d8","entry":"random_learning","repo":"ipeis/HH-VAEM","repo_kind":"official","path":"src/mutual_information.py","file_url":"https://github.com/ipeis/HH-VAEM/blob/HEAD/src/mutual_information.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":"c154239dbec460d8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}