Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms — DORM, DORM+, and AdaHedgeD — arise from a novel reduction of delayed online learning to optimistic online learning that reveals how optimistic hints can mitigate the regret penalty caused by delay. We pair this delay-as-optimism perspective with a new analysis of optimistic learning that exposes its robustness to hinting errors and a new meta-algorithm for learning effective hinting strategies in the presence of delay. We conclude by benchmarking our algorithms on four subseasonal climate forecasting tasks, demonstrating low regret relative to state-of-the-art forecasting models.<\/p>\n","protected":false},"excerpt":{"rendered":"
Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms — DORM, DORM+, and AdaHedgeD — arise from a novel reduction of delayed online learning to optimistic online learning that reveals how optimistic hints […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[246694,251857,246691,246685,246844,257986,255385,246823,246754,257983],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-760393","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-field-of-study-artificial-intelligence","msr-field-of-study-benchmarking","msr-field-of-study-computer-science","msr-field-of-study-machine-learning","msr-field-of-study-online-learning","msr-field-of-study-optimism","msr-field-of-study-reduction-complexity","msr-field-of-study-regret","msr-field-of-study-robustness-computer-science","msr-field-of-study-weather-forecasting"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-7-17","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2106.06885","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Genevieve Flaspohler","user_id":0,"rest_url":false},{"type":"text","value":"Francesco Orabona","user_id":0,"rest_url":false},{"type":"text","value":"Judah Cohen","user_id":0,"rest_url":false},{"type":"text","value":"Soukayna Mouatadid","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Miruna Oprescu","user_id":37353,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Miruna Oprescu"},{"type":"text","value":"Paulo Orenstein","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Lester Mackey","user_id":36161,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Lester Mackey"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[740803],"msr_group":[847351],"msr_project":[608757],"publication":[],"video":[],"download":[778384],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":608757,"post_title":"Subseasonal Climate Forecasting","post_name":"subseasonal-climate-forecasting","post_type":"msr-project","post_date":"2019-09-16 06:15:14","post_modified":"2023-07-27 12:48:19","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/subseasonal-climate-forecasting\/","post_excerpt":"Water managers in the western United States rely on long-term forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. 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