{"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/variational-auto-regressive-gaussian","title":"Variational Auto-Regressive Gaussian Processes for Continual Learning","arxiv_id":"2006.05468","date":"2020-06-09","proceeding":null,"authors":["Sanyam Kapoor","Theofanis Karaletsos","Thang D. Bui"],"abstract":"Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy estimates show VAR-GPs prevent catastrophic forgetting, which is empirically supported by strong performance on modern continual learning benchmarks against competitive baselines. A thorough ablation study demonstrates the efficacy of our modeling choices.","url_abs":"https://arxiv.org/abs/2006.05468v3","url_pdf":"https://arxiv.org/pdf/2006.05468v3.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":"variational-auto-regressive-gaussian","repo_url":"https://github.com/uber-research/vargp","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.05468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05468"}},"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/uber-research/vargp","reach":null}],"summary":{"ran":3,"ran_draft_wrong":7,"unverified":1},"by_repo_kind":{"official":{"samples":11,"ran":10,"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":"83a90dd71ef26edf","entry":"DeepRBFKernel","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"83a90dd71ef26edf"}},{"code_sha256_prefix":"7054eeeb5bbdcb29","entry":"MulticlassSoftmax","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7054eeeb5bbdcb29"}},{"code_sha256_prefix":"bcc66d9ddfa88032","entry":"RBFKernel","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"bcc66d9ddfa88032"}},{"code_sha256_prefix":"09e7ee9467bbb608","entry":"cholesky","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"09e7ee9467bbb608"}},{"code_sha256_prefix":"3c32ec62ac281327","entry":"gp_cond","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3c32ec62ac281327"}},{"code_sha256_prefix":"5bbde606973fa2d6","entry":"linear_joint","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"5bbde606973fa2d6"}},{"code_sha256_prefix":"b2df6ebf5ba61d4d","entry":"linear_marginal_diag","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b2df6ebf5ba61d4d"}},{"code_sha256_prefix":"c00b44c81cb07664","entry":"mat2trilvec","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c00b44c81cb07664"}},{"code_sha256_prefix":"07328e40f0f0f5dd","entry":"rev_cholesky","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"07328e40f0f0f5dd"}},{"code_sha256_prefix":"ecbc8bc8c4ea9420","entry":"vec2tril","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ecbc8bc8c4ea9420"}},{"code_sha256_prefix":"c01a80178407fc13","entry":"VARGP","repo":"uber-research/vargp","repo_kind":"official","path":"var_gp/vargp.py","file_url":"https://github.com/uber-research/vargp/blob/HEAD/var_gp/vargp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c01a80178407fc13"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}