{"url":"/task/global-optimization","name":"global-optimization","slug":"global-optimization","description_markdown":null,"categories":[],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":658,"papers_with_code":158,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":158,"tagged_in_all":658,"items":[{"url":"/paper/tree-search-vs-optimization-approaches-for","title":"Tree Search vs Optimization Approaches for Map Generation","date":"2019-03-27","arxiv_id":"1903.11678","repositories_listed":5,"syntology":null},{"url":"/paper/automatic-prior-selection-for-meta-bayesian","title":"Pre-trained Gaussian Processes for Bayesian Optimization","date":"2021-09-16","arxiv_id":"2109.08215","repositories_listed":4,"syntology":{"n":14,"n_ran":3,"n_unverified":11,"n_pointer_only":2}},{"url":"/paper/scalable-bayesian-optimization-using-deep","title":"Scalable Bayesian Optimization Using Deep Neural Networks","date":"2015-02-19","arxiv_id":"1502.05700","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/a-literature-survey-of-benchmark-functions","title":"A Literature Survey of Benchmark Functions For Global Optimization Problems","date":"2013-08-19","arxiv_id":"1308.4008","repositories_listed":4,"syntology":null},{"url":"/paper/fed-gloss-dp-federated-global-learning-using","title":"FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations","date":"2023-02-02","arxiv_id":"2302.01068","repositories_listed":3,"syntology":{"n":21,"n_ran":0,"n_unverified":21,"n_pointer_only":0}},{"url":"/paper/scalable-global-optimization-via-local","title":"Scalable Global Optimization via Local Bayesian Optimization","date":"2019-10-03","arxiv_id":"1910.01739","repositories_listed":3,"syntology":null},{"url":"/paper/pysot-and-poap-an-event-driven-asynchronous","title":"pySOT and POAP: An event-driven asynchronous framework for surrogate optimization","date":"2019-07-30","arxiv_id":"1908.00420","repositories_listed":3,"syntology":null},{"url":"/paper/global-optimization-of-lipschitz-functions","title":"Global optimization of Lipschitz functions","date":"2017-03-07","arxiv_id":"1703.02628","repositories_listed":3,"syntology":{"n":3,"n_ran":1,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/learning-geometric-transformation-for-point","title":"Learning Geometric Transformation for Point Cloud Completion","date":"2023-06-08","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/necessary-and-sufficient-conditions-for-41","title":"Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming","date":"2023-05-31","arxiv_id":"2305.19706","repositories_listed":2,"syntology":null},{"url":"/paper/tsfool-crafting-high-quality-adversarial-time","title":"TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack","date":"2022-09-14","arxiv_id":"2209.06388","repositories_listed":2,"syntology":null},{"url":"/paper/certifiable-outlier-robust-geometric","title":"Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization","date":"2021-09-07","arxiv_id":"2109.03349","repositories_listed":2,"syntology":null},{"url":"/paper/document-level-event-extraction-via-parallel","title":"Document-level Event Extraction via Parallel Prediction Networks","date":"2021-08-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/scalable-and-flexible-deep-bayesian","title":"Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure","date":"2021-04-23","arxiv_id":"2104.11667","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_unverified":1,"n_pointer_only":4}},{"url":"/paper/pathwise-conditioning-of-gaussian-processes","title":"Pathwise Conditioning of Gaussian Processes","date":"2020-11-08","arxiv_id":"2011.04026","repositories_listed":2,"syntology":null},{"url":"/paper/hyperparameter-optimization-via-sequential","title":"Hyperparameter Optimization via Sequential Uniform Designs","date":"2020-09-08","arxiv_id":"2009.03586","repositories_listed":2,"syntology":null},{"url":"/paper/efficient-rollout-strategies-for-bayesian","title":"Efficient Rollout Strategies for Bayesian Optimization","date":"2020-02-24","arxiv_id":"2002.10539","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/botorch-programmable-bayesian-optimization-in","title":"BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization","date":"2019-10-14","arxiv_id":"1910.06403","repositories_listed":2,"syntology":null},{"url":"/paper/slot-gated-modeling-for-joint-slot-filling","title":"Slot-Gated Modeling for Joint Slot Filling and Intent Prediction","date":"2018-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/nerf-based-cbct-reconstruction-needs","title":"NeRF-based CBCT Reconstruction needs Normalization and Initialization","date":"2025-06-24","arxiv_id":"2506.19742","repositories_listed":1,"syntology":null},{"url":"/paper/focusing-on-tracks-for-online-multi-object","title":"Focusing on Tracks for Online Multi-Object Tracking","date":"2025-06-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-ga-llm-framework-for-structured-task","title":"A Hybrid GA LLM Framework for Structured Task Optimization","date":"2025-06-09","arxiv_id":"2506.07483","repositories_listed":1,"syntology":null},{"url":"/paper/a-divide-and-conquer-approach-for-global","title":"A Divide-and-Conquer Approach for Global Orientation of Non-Watertight Scene-Level Point Clouds Using 0-1 Integer Optimization","date":"2025-05-29","arxiv_id":"2505.23469","repositories_listed":1,"syntology":null},{"url":"/paper/improving-llm-based-global-optimization-with","title":"Improving LLM-based Global Optimization with Search Space Partitioning","date":"2025-05-27","arxiv_id":"2505.21372","repositories_listed":1,"syntology":null},{"url":"/paper/physics-guided-and-fabrication-aware-inverse","title":"Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models","date":"2025-04-23","arxiv_id":"2504.17077","repositories_listed":1,"syntology":null},{"url":"/paper/sudo-enhancing-text-to-image-diffusion-models","title":"SUDO: Enhancing Text-to-Image Diffusion Models with Self-Supervised Direct Preference Optimization","date":"2025-04-20","arxiv_id":"2504.14534","repositories_listed":1,"syntology":null},{"url":"/paper/probability-estimation-and-scheduling","title":"Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System","date":"2025-04-10","arxiv_id":"2504.07453","repositories_listed":1,"syntology":null},{"url":"/paper/riemannian-optimization-on-relaxed-indicator","title":"Riemannian Optimization on Relaxed Indicator Matrix Manifold","date":"2025-03-26","arxiv_id":"2503.20505","repositories_listed":1,"syntology":null},{"url":"/paper/fedawa-adaptive-optimization-of-aggregation","title":"FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors","date":"2025-03-20","arxiv_id":"2503.15842","repositories_listed":1,"syntology":null},{"url":"/paper/island-based-evolutionary-computation-with","title":"Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven Optimization","date":"2025-03-17","arxiv_id":"2503.12856","repositories_listed":1,"syntology":null}],"syntology_records":6,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}