{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:11Z","timestamp":1761176231395,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>We formalize sequential decision\u2013making with information acquisition as the Probing-augmented User-Centric Selection (PUCS) framework, where a learner first probes a subset of arms to obtain side information on resources and rewards, and then assigns K plays to M arms. PUCS encompasses practical scenarios such as ridesharing, wireless scheduling, and content recommendation, in which both resources and payoffs are initially unknown and probing incurs cost. For the offline setting (known payoff distributions), we present a greedy probing algorithm with a constant-factor approximation guarantee of \u03b6=(e-1)\/(2e-1). For the online setting (unknown payoff distributions), we introduce OLPA, a stochastic combinatorial bandit algorithm that achieves a regret bound of O(\u221aT+ln2T). We also prove an \u03a9(\u221aT) lower bound, showing that the upper bound is tight up to logarithmic factors. Numerical results using two real-world datasets demonstrate the effectiveness of our solutions.<\/jats:p>","DOI":"10.3233\/faia251181","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:49Z","timestamp":1761126829000},"source":"Crossref","is-referenced-by-count":0,"title":["Online Learning with Probing for Sequential User-Centric Selection"],"prefix":"10.3233","author":[{"given":"Tianyi","family":"Xu","sequence":"first","affiliation":[{"name":"Tulane University, United States"}]},{"given":"Yiting","family":"Chen","sequence":"additional","affiliation":[{"name":"Boston University, United States"}]},{"given":"Henger","family":"Li","sequence":"additional","affiliation":[{"name":"Tulane University, United States"}]},{"given":"Zheyong","family":"Bian","sequence":"additional","affiliation":[{"name":"University of Houston, United States"}]},{"given":"Emiliano","family":"Dall\u2019Anese","sequence":"additional","affiliation":[{"name":"Boston University, United States"}]},{"given":"Zizhan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tulane University, United States"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251181","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:49Z","timestamp":1761126829000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251181"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251181","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}