{"ID":6537737,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11210","arxiv_id":"2607.11210","title":"Decoupling Corruption and Horizon in Robust Contextual Pricing","abstract":"We study robust repeated contextual pricing, where valuations depends linearly on the features. At each round $t\\in[T]$, a seller observes a context, posts a price, and receives only a possibly corrupted binary sale feedback. The seller knows an upper bound $C$ on the number of corrupted rounds. We design an algorithm with regret $\\mathcal O(Cd+d^2\\log T)$, where $d$ is the context dimension. This is the first guarantee for robust contextual pricing that separates the dependence on the corruption budget $C$ from the horizon $T$, closing the problem left open by Gupta, Guruganesh, Paes Leme, and Schneider (2025).","short_abstract":"We study robust repeated contextual pricing, where valuations depends linearly on the features. At each round $t\\in[T]$, a seller observes a context, posts a price, and receives only a possibly corrupted binary sale feedback. The seller knows an upper bound $C$ on the number of corrupted rounds. We design an algorithm...","url_abs":"https://arxiv.org/abs/2607.11210","url_pdf":"https://arxiv.org/pdf/2607.11210v1","authors":"[\"Matteo Castiglioni\",\"Francesco Emanuele Stradi\"]","published":"2026-07-13T08:02:18Z","proceeding":"cs.GT","tasks":"[\"cs.GT\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}