The content discusses the challenges of online learning under budget and ROI constraints, proposing a new framework with weakly adaptive regret minimizers. By circumventing unrealistic assumptions, the framework ensures no-regret guarantees in both stochastic and adversarial settings. The approach is applied to bidding in various auction mechanisms, demonstrating its effectiveness.
Existing primal-dual algorithms for constrained online learning problems rely on unrealistic assumptions about known parameters related to feasibility. The proposed framework introduces weakly adaptive regret minimizers to overcome these limitations.
The study shows how this new approach provides best-of-both-worlds no-regret guarantees even without prior knowledge of certain parameters. It offers solutions for bidding in practical scenarios like ad auctions under budget and ROI constraints.
Key points include the introduction of weak adaptivity in primal-dual frameworks, ensuring boundedness of Lagrange multipliers without prior knowledge of Slater's parameter α. The study demonstrates the effectiveness of this approach in optimizing bids in various auction mechanisms.
The content highlights the importance of safe policies in ensuring constraint satisfaction without requiring unrealistic assumptions about known parameters. By relaxing these assumptions, the proposed framework offers robust solutions for online learning under budget and ROI constraints.
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by Matteo Casti... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2302.01203.pdfDeeper Inquiries