{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/bilevel-optimization/papers/ran/1","list_of":"/task/bilevel-optimization","task":"Bilevel Optimization","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,56],"of":56,"counts":{"archive_papers_tagged":423,"with_a_code_link":143,"where_syntology_ran_a_sample":56,"not_listed_spam_title":0,"listed":423,"listed_where_code_ran":56,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":45,"every_run_a_failure_of_syntologys_instrument":11,"listed_with_a_run_with_no_instrument_failure":45,"listed_every_run_a_failure_of_syntologys_instrument":11,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/bilevel-optimization/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/gcal-adapting-graph-models-to-evolving-domain","slug":"gcal-adapting-graph-models-to-evolving-domain","title":"GCAL: Adapting Graph Models to Evolving Domain Shifts","date":"2025-05-22","arxiv_id":"2505.16860","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":1,"n_ran_checked":4,"n_instrument":3,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":10,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/gcal-adapting-graph-models-to-evolving-domain#ran","syntology_url":"https://syntology.ai/paper/2505.16860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.16860"}},"official":{"repos":["joe817/gcal"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/projecting-assumptions-the-duality-between","slug":"projecting-assumptions-the-duality-between","title":"Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry","date":"2025-03-03","arxiv_id":"2503.01822","repositories_listed":0,"syntology":{"n":37,"n_ran":26,"n_constructed":3,"n_ran_checked":13,"n_instrument":13,"n_unverified":11,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":4,"phrase":"26 ran (of which 3 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 13 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/projecting-assumptions-the-duality-between#ran","syntology_url":"https://syntology.ai/paper/2503.01822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.01822"}},"official":null}},{"url":"/paper/prdp-progressively-refined-differentiable","slug":"prdp-progressively-refined-differentiable","title":"PRDP: Progressively Refined Differentiable Physics","date":"2025-02-26","arxiv_id":"2502.19611","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":3,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/prdp-progressively-refined-differentiable#ran","syntology_url":"https://syntology.ai/paper/2502.19611","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.19611"}},"official":{"repos":["tum-pbs/prdp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-training-for-defense-against","slug":"adversarial-training-for-defense-against","title":"Adversarial Training for Defense Against Label Poisoning Attacks","date":"2025-02-24","arxiv_id":"2502.17121","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-training-for-defense-against#ran","syntology_url":"https://syntology.ai/paper/2502.17121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.17121"}},"official":{"repos":["melisilaydabal/floral"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-nearly-optimal-single-loop-algorithm-for","slug":"a-nearly-optimal-single-loop-algorithm-for","title":"A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness","date":"2024-12-28","arxiv_id":"2412.20017","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-nearly-optimal-single-loop-algorithm-for#ran","syntology_url":"https://syntology.ai/paper/2412.20017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.20017"}},"official":{"repos":["MingruiLiu-ML-Lab/Single-Loop-bilevel-Optimizer-under-Unbounded-Smoothness"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/exact-certification-of-graph-neural-networks","slug":"exact-certification-of-graph-neural-networks","title":"Exact Certification of (Graph) Neural Networks Against Label Poisoning","date":"2024-11-30","arxiv_id":"2412.00537","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exact-certification-of-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2412.00537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.00537"}},"official":{"repos":["saper0/qpcert"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fair-bilevel-neural-network-fairbinn-on","slug":"fair-bilevel-neural-network-fairbinn-on","title":"Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium","date":"2024-10-21","arxiv_id":"2410.16432","repositories_listed":2,"syntology":{"n":20,"n_ran":16,"n_constructed":3,"n_ran_checked":13,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":4,"phrase":"16 ran (of which 3 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fair-bilevel-neural-network-fairbinn-on#ran","syntology_url":"https://syntology.ai/paper/2410.16432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.16432"}},"official":{"repos":["yazdanimehdi/fairbinn"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":1,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/unseg-one-universal-unlearnable-example","slug":"unseg-one-universal-unlearnable-example","title":"UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation","date":"2024-10-13","arxiv_id":"2410.09909","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/unseg-one-universal-unlearnable-example#ran","syntology_url":"https://syntology.ai/paper/2410.09909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09909"}},"official":null}},{"url":"/paper/seal-safety-enhanced-aligned-llm-fine-tuning","slug":"seal-safety-enhanced-aligned-llm-fine-tuning","title":"SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection","date":"2024-10-09","arxiv_id":"2410.07471","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/seal-safety-enhanced-aligned-llm-fine-tuning#ran","syntology_url":"https://syntology.ai/paper/2410.07471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07471"}},"official":{"repos":["hanshen95/seal"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/an-accelerated-algorithm-for-stochastic","slug":"an-accelerated-algorithm-for-stochastic","title":"An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness","date":"2024-09-28","arxiv_id":"2409.19212","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/an-accelerated-algorithm-for-stochastic#ran","syntology_url":"https://syntology.ai/paper/2409.19212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19212"}},"official":{"repos":["mingruiliu-ml-lab/accelerated-bilevel-optimization-unbounded-smoothness"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/loss-distillation-via-gradient-matching-for","slug":"loss-distillation-via-gradient-matching-for","title":"Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance","date":"2024-09-10","arxiv_id":"2409.06171","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/loss-distillation-via-gradient-matching-for#ran","syntology_url":"https://syntology.ai/paper/2409.06171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.06171"}},"official":{"repos":["zhang-vislab/iros2024-lossdistillationweightedcd"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-sam-requires-rethinking-its","slug":"improving-sam-requires-rethinking-its","title":"Improving SAM Requires Rethinking its Optimization Formulation","date":"2024-07-17","arxiv_id":"2407.12993","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-sam-requires-rethinking-its#ran","syntology_url":"https://syntology.ai/paper/2407.12993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12993"}},"official":{"repos":["LIONS-EPFL/BiSAM"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/provable-robustness-of-graph-neural-networks","slug":"provable-robustness-of-graph-neural-networks","title":"Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks","date":"2024-07-15","arxiv_id":"2407.10867","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/provable-robustness-of-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2407.10867","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10867"}},"official":{"repos":["saper0/qpcert"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-diffusion-at-lightspeed","slug":"learning-diffusion-at-lightspeed","title":"Learning diffusion at lightspeed","date":"2024-06-18","arxiv_id":"2406.12616","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-diffusion-at-lightspeed#ran","syntology_url":"https://syntology.ai/paper/2406.12616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12616"}},"official":null}},{"url":"/paper/a-primal-dual-assisted-penalty-approach-to","slug":"a-primal-dual-assisted-penalty-approach-to","title":"A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints","date":"2024-06-14","arxiv_id":"2406.10148","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-primal-dual-assisted-penalty-approach-to#ran","syntology_url":"https://syntology.ai/paper/2406.10148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10148"}},"official":{"repos":["Liuyuan999/Penalty_Based_Lagrangian_Bilevel"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/stochastic-bilevel-optimization-with-lower","slug":"stochastic-bilevel-optimization-with-lower","title":"Contextual Bilevel Reinforcement Learning for Incentive Alignment","date":"2024-06-03","arxiv_id":"2406.01575","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/stochastic-bilevel-optimization-with-lower#ran","syntology_url":"https://syntology.ai/paper/2406.01575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01575"}},"official":{"repos":["lasgroup/hpgd"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/gs-phong-meta-learned-3d-gaussians-for","slug":"gs-phong-meta-learned-3d-gaussians-for","title":"GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis","date":"2024-05-31","arxiv_id":"2405.20791","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":8,"n_pointer_only":13,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/gs-phong-meta-learned-3d-gaussians-for#ran","syntology_url":"https://syntology.ai/paper/2405.20791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20791"}},"official":{"repos":["ymhe12/GS-Phong"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/lancbio-dynamic-lanczos-aided-bilevel","slug":"lancbio-dynamic-lanczos-aided-bilevel","title":"LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace","date":"2024-04-04","arxiv_id":"2404.03331","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/lancbio-dynamic-lanczos-aided-bilevel#ran","syntology_url":"https://syntology.ai/paper/2404.03331","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03331"}},"official":{"repos":["ucas-yanyang/lancbio"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/functional-bilevel-optimization-for-machine","slug":"functional-bilevel-optimization-for-machine","title":"Functional Bilevel Optimization for Machine Learning","date":"2024-03-29","arxiv_id":"2403.20233","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/functional-bilevel-optimization-for-machine#ran","syntology_url":"https://syntology.ai/paper/2403.20233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.20233"}},"official":{"repos":["inria-thoth/funcbo"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-framework-for-bilevel-optimization-on","slug":"a-framework-for-bilevel-optimization-on","title":"A Framework for Bilevel Optimization on Riemannian Manifolds","date":"2024-02-06","arxiv_id":"2402.03883","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-framework-for-bilevel-optimization-on#ran","syntology_url":"https://syntology.ai/paper/2402.03883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03883"}},"official":{"repos":["andyjm3/rhgd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bilevel-optimization-under-unbounded","slug":"bilevel-optimization-under-unbounded","title":"Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis","date":"2024-01-17","arxiv_id":"2401.09587","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/bilevel-optimization-under-unbounded#ran","syntology_url":"https://syntology.ai/paper/2401.09587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09587"}},"official":{"repos":["mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/convex-and-bilevel-optimization-for-neuro","slug":"convex-and-bilevel-optimization-for-neuro","title":"Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning","date":"2024-01-17","arxiv_id":"2401.09651","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/convex-and-bilevel-optimization-for-neuro#ran","syntology_url":"https://syntology.ai/paper/2401.09651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09651"}},"official":{"repos":["convexbilevelnesylearning/experimentscripts","convexbilevelnesylearning/psl","linqs/dickens-icml24"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/toward-robust-imperceptible-perturbation","slug":"toward-robust-imperceptible-perturbation","title":"MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning","date":"2023-11-22","arxiv_id":"2311.13127","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/toward-robust-imperceptible-perturbation#ran","syntology_url":"https://syntology.ai/paper/2311.13127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13127"}},"official":{"repos":["liuyixin-louis/metacloak"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/embarassingly-simple-dataset-distillation","slug":"embarassingly-simple-dataset-distillation","title":"Embarassingly Simple Dataset Distillation","date":"2023-11-13","arxiv_id":"2311.07025","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/embarassingly-simple-dataset-distillation#ran","syntology_url":"https://syntology.ai/paper/2311.07025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07025"}},"official":{"repos":["fengyzpku/simple_dataset_distillation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-set-representation-learning","slug":"self-supervised-set-representation-learning","title":"Self-Supervised Dataset Distillation for Transfer Learning","date":"2023-10-10","arxiv_id":"2310.06511","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-set-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2310.06511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.06511"}},"official":{"repos":["db-lee/selfsup_dd"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/squeeze-recover-and-relabel-dataset","slug":"squeeze-recover-and-relabel-dataset","title":"Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective","date":"2023-06-22","arxiv_id":"2306.13092","repositories_listed":2,"syntology":{"n":6,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/squeeze-recover-and-relabel-dataset#ran","syntology_url":"https://syntology.ai/paper/2306.13092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13092"}},"official":{"repos":["VILA-Lab/SRe2L"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"url":"/paper/from-hypergraph-energy-functions-to","slug":"from-hypergraph-energy-functions-to","title":"From Hypergraph Energy Functions to Hypergraph Neural Networks","date":"2023-06-16","arxiv_id":"2306.09623","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/from-hypergraph-energy-functions-to#ran","syntology_url":"https://syntology.ai/paper/2306.09623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09623"}},"official":{"repos":["yxzwang/phenomnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/boosting-differentiable-causal-discovery-via","slug":"boosting-differentiable-causal-discovery-via","title":"Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting","date":"2023-03-06","arxiv_id":"2303.03187","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/boosting-differentiable-causal-discovery-via#ran","syntology_url":"https://syntology.ai/paper/2303.03187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.03187"}},"official":{"repos":["anzhang314/rescore"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-penalty-based-bilevel-gradient-descent","slug":"on-penalty-based-bilevel-gradient-descent","title":"On Penalty-based Bilevel Gradient Descent Method","date":"2023-02-10","arxiv_id":"2302.05185","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":3,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 3 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-penalty-based-bilevel-gradient-descent#ran","syntology_url":"https://syntology.ai/paper/2302.05185","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.05185"}},"official":{"repos":["hanshen95/penalized-bilevel-gradient-descent"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/asynchronous-distributed-bilevel-optimization","slug":"asynchronous-distributed-bilevel-optimization","title":"Asynchronous Distributed Bilevel Optimization","date":"2022-12-20","arxiv_id":"2212.10048","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/asynchronous-distributed-bilevel-optimization#ran","syntology_url":"https://syntology.ai/paper/2212.10048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.10048"}},"official":{"repos":["iclr23submission6251/adbo"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/personalized-decentralized-bilevel","slug":"personalized-decentralized-bilevel","title":"Decentralized Hyper-Gradient Computation over Time-Varying Directed Networks","date":"2022-10-05","arxiv_id":"2210.02129","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/personalized-decentralized-bilevel#ran","syntology_url":"https://syntology.ai/paper/2210.02129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02129"}},"official":{"repos":["hitachi-rd-cv/pdbo-hgp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/min-max-bilevel-multi-objective-optimization","slug":"min-max-bilevel-multi-objective-optimization","title":"Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning","date":"2022-03-03","arxiv_id":"2203.01924","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/min-max-bilevel-multi-objective-optimization#ran","syntology_url":"https://syntology.ai/paper/2203.01924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01924"}},"official":{"repos":["minimario/morbit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/bilevel-optimization-with-a-lower-level","slug":"bilevel-optimization-with-a-lower-level","title":"Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start","date":"2022-02-07","arxiv_id":"2202.03397","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":2,"n_instrument":6,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bilevel-optimization-with-a-lower-level#ran","syntology_url":"https://syntology.ai/paper/2202.03397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03397"}},"official":{"repos":["csml-iit-ucl/bioptexps"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-invariant-networks-with-differentiable","slug":"deep-invariant-networks-with-differentiable","title":"Deep invariant networks with differentiable augmentation layers","date":"2022-02-04","arxiv_id":"2202.02142","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/deep-invariant-networks-with-differentiable#ran","syntology_url":"https://syntology.ai/paper/2202.02142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.02142"}},"official":{"repos":["cedricrommel/augnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-framework-for-bilevel-optimization-that","slug":"a-framework-for-bilevel-optimization-that","title":"A framework for bilevel optimization that enables stochastic and global variance reduction algorithms","date":"2022-01-31","arxiv_id":"2201.13409","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-framework-for-bilevel-optimization-that#ran","syntology_url":"https://syntology.ai/paper/2201.13409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.13409"}},"official":{"repos":["benchopt/benchmark_bilevel"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/calibrated-hyperspectral-image-reconstruction","slug":"calibrated-hyperspectral-image-reconstruction","title":"Modeling Mask Uncertainty in Hyperspectral Image Reconstruction","date":"2021-12-31","arxiv_id":"2112.15362","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/calibrated-hyperspectral-image-reconstruction#ran","syntology_url":"https://syntology.ai/paper/2112.15362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.15362"}},"official":{"repos":["jiamian-wang/mask_uncertainty_spectral_sci"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/towards-evaluating-the-robustness-of-neural-2","slug":"towards-evaluating-the-robustness-of-neural-2","title":"Towards Evaluating the Robustness of Neural Networks Learned by Transduction","date":"2021-10-27","arxiv_id":"2110.14735","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/towards-evaluating-the-robustness-of-neural-2#ran","syntology_url":"https://syntology.ai/paper/2110.14735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14735"}},"official":{"repos":["jfc43/eval-transductive-robustness"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/es-based-jacobian-enables-faster-bilevel-1","slug":"es-based-jacobian-enables-faster-bilevel-1","title":"On the Convergence Theory for Hessian-Free Bilevel Algorithms","date":"2021-10-13","arxiv_id":"2110.07004","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/es-based-jacobian-enables-faster-bilevel-1#ran","syntology_url":"https://syntology.ai/paper/2110.07004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07004"}},"official":{"repos":["sowmaster/esjacobians"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-gradient-based-bilevel-optimization","slug":"towards-gradient-based-bilevel-optimization","title":"Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond","date":"2021-10-01","arxiv_id":"2110.00455","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-gradient-based-bilevel-optimization#ran","syntology_url":"https://syntology.ai/paper/2110.00455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.00455"}},"official":{"repos":["vis-opt-group/iaptt-gm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bobcat-bilevel-optimization-based","slug":"bobcat-bilevel-optimization-based","title":"BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing","date":"2021-08-17","arxiv_id":"2108.07386","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":3,"n_ran_checked":5,"n_instrument":5,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"10 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bobcat-bilevel-optimization-based#ran","syntology_url":"https://syntology.ai/paper/2108.07386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07386"}},"official":{"repos":["arghosh/bobcat"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/provably-faster-algorithms-for-bilevel","slug":"provably-faster-algorithms-for-bilevel","title":"Provably Faster Algorithms for Bilevel Optimization","date":"2021-06-08","arxiv_id":"2106.04692","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":2,"n_instrument":6,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/provably-faster-algorithms-for-bilevel#ran","syntology_url":"https://syntology.ai/paper/2106.04692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04692"}},"official":{"repos":["JunjieYang97/MRVRBO"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/bifair-training-fair-models-with-bilevel","slug":"bifair-training-fair-models-with-bilevel","title":"Fair Machine Learning under Limited Demographically Labeled Data","date":"2021-06-03","arxiv_id":"2106.04757","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bifair-training-fair-models-with-bilevel#ran","syntology_url":"https://syntology.ai/paper/2106.04757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04757"}},"official":{"repos":["TinfoilHat0/BiFair"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-differentiation-for-fast","slug":"implicit-differentiation-for-fast","title":"Implicit differentiation for fast hyperparameter selection in non-smooth convex learning","date":"2021-05-04","arxiv_id":"2105.01637","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/implicit-differentiation-for-fast#ran","syntology_url":"https://syntology.ai/paper/2105.01637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.01637"}},"official":{"repos":["QB3/sparse-ho"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fairbatch-batch-selection-for-model-fairness-1","slug":"fairbatch-batch-selection-for-model-fairness-1","title":"FairBatch: Batch Selection for Model Fairness","date":"2020-12-03","arxiv_id":"2012.01696","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/fairbatch-batch-selection-for-model-fairness-1#ran","syntology_url":"https://syntology.ai/paper/2012.01696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.01696"}},"official":{"repos":["yuji-roh/fairbatch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/delta-stn-efficient-bilevel-optimization-for","slug":"delta-stn-efficient-bilevel-optimization-for","title":"Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians","date":"2020-10-26","arxiv_id":"2010.13514","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/delta-stn-efficient-bilevel-optimization-for#ran","syntology_url":"https://syntology.ai/paper/2010.13514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13514"}},"official":{"repos":["pomonam/Self-Tuning-Networks"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-batch-active-learning-via","slug":"semi-supervised-batch-active-learning-via","title":"Semi-supervised Batch Active Learning via Bilevel Optimization","date":"2020-10-19","arxiv_id":"2010.09654","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semi-supervised-batch-active-learning-via#ran","syntology_url":"https://syntology.ai/paper/2010.09654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09654"}},"official":{"repos":["zalanborsos/bilevel_coresets"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/provably-faster-algorithms-for-bilevel-1","slug":"provably-faster-algorithms-for-bilevel-1","title":"Bilevel Optimization: Convergence Analysis and Enhanced Design","date":"2020-10-15","arxiv_id":"2010.07962","repositories_listed":2,"syntology":{"n":18,"n_ran":14,"n_constructed":0,"n_ran_checked":7,"n_instrument":7,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 7 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/provably-faster-algorithms-for-bilevel-1#ran","syntology_url":"https://syntology.ai/paper/2010.07962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07962"}},"official":{"repos":["junjieyang97/stocbio_hp"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/adversarial-immunization-for-improving","slug":"adversarial-immunization-for-improving","title":"Adversarial Immunization for Certifiable Robustness on Graphs","date":"2020-07-19","arxiv_id":"2007.09647","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-immunization-for-improving#ran","syntology_url":"https://syntology.ai/paper/2007.09647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09647"}},"official":{"repos":["TaoShuchang/AdvImmune"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-play-sequential-games-versus","slug":"learning-to-play-sequential-games-versus","title":"Learning to Play Sequential Games versus Unknown Opponents","date":"2020-07-10","arxiv_id":"2007.05271","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-to-play-sequential-games-versus#ran","syntology_url":"https://syntology.ai/paper/2007.05271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05271"}},"official":null}},{"url":"/paper/learning-data-augmentation-with-online","slug":"learning-data-augmentation-with-online","title":"Learning Data Augmentation with Online Bilevel Optimization for Image Classification","date":"2020-06-25","arxiv_id":"2006.14699","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-data-augmentation-with-online#ran","syntology_url":"https://syntology.ai/paper/2006.14699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.14699"}},"official":{"repos":["ElementAI/bilevel_augment"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/coresets-via-bilevel-optimization-for","slug":"coresets-via-bilevel-optimization-for","title":"Coresets via Bilevel Optimization for Continual Learning and Streaming","date":"2020-06-06","arxiv_id":"2006.03875","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/coresets-via-bilevel-optimization-for#ran","syntology_url":"https://syntology.ai/paper/2006.03875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03875"}},"official":{"repos":["zalanborsos/bilevel_coresets"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/metapoison-practical-general-purpose-clean","slug":"metapoison-practical-general-purpose-clean","title":"MetaPoison: Practical General-purpose Clean-label Data Poisoning","date":"2020-04-01","arxiv_id":"2004.00225","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/metapoison-practical-general-purpose-clean#ran","syntology_url":"https://syntology.ai/paper/2004.00225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.00225"}},"official":{"repos":["wronnyhuang/metapoison"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/truncated-back-propagation-for-bilevel","slug":"truncated-back-propagation-for-bilevel","title":"Truncated Back-propagation for Bilevel Optimization","date":"2018-10-25","arxiv_id":"1810.10667","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/truncated-back-propagation-for-bilevel#ran","syntology_url":"https://syntology.ai/paper/1810.10667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.10667"}},"official":null}},{"url":"/paper/deep-bilevel-learning","slug":"deep-bilevel-learning","title":"Deep Bilevel Learning","date":"2018-09-05","arxiv_id":"1809.01465","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-bilevel-learning#ran","syntology_url":"https://syntology.ai/paper/1809.01465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.01465"}},"official":null}},{"url":"/paper/spectral-inference-networks-unifying-spectral","slug":"spectral-inference-networks-unifying-spectral","title":"Spectral Inference Networks: Unifying Deep and Spectral Learning","date":"2018-06-06","arxiv_id":"1806.02215","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/spectral-inference-networks-unifying-spectral#ran","syntology_url":"https://syntology.ai/paper/1806.02215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02215"}},"official":{"repos":["deepmind/spectral_inference_networks"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/optnet-differentiable-optimization-as-a-layer","slug":"optnet-differentiable-optimization-as-a-layer","title":"OptNet: Differentiable Optimization as a Layer in Neural Networks","date":"2017-03-01","arxiv_id":"1703.00443","repositories_listed":8,"syntology":{"n":16,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/optnet-differentiable-optimization-as-a-layer#ran","syntology_url":"https://syntology.ai/paper/1703.00443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.00443"}},"official":{"repos":["locuslab/optnet","Kyubyong/sudoku"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"b0a187bfd9735279f299fe708f41e9067c05c71d3fdbf79e196c2da1b2793778","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}