论文标题
无预测的强盗优化及其隐私保证
Projection-Free Bandit Optimization with Privacy Guarantees
论文作者
论文摘要
我们在无投影设置中设计了针对Bandit凸优化问题的不同私有算法。每当决策集具有复杂的几何形状时，此设置很重要，并且仅通过线性优化的甲骨文有效地进行访问，因此欧几里得投影不可用（例如，矩阵多面体，下层基础多层）。这是第一个用于无投影强盗优化的差异易捕获算法，实际上，我们属于$ \ widetilde {o}（t^{3/4}）$匹配的匹配的非著名的非预测的无预测算法（Garber-kretzu，garber-kretzu，aistats'20），并且是最不知名的私人algorith的设置，甚至是为了投影，甚至是five fives fivestive and Algorith。 （Smith-Thakurta，Neurips`13）。
We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. This setting is important whenever the decision set has a complex geometry, and access to it is done efficiently only through a linear optimization oracle, hence Euclidean projections are unavailable (e.g. matroid polytope, submodular base polytope). This is the first differentially-private algorithm for projection-free bandit optimization, and in fact our bound of $\widetilde{O}(T^{3/4})$ matches the best known non-private projection-free algorithm (Garber-Kretzu, AISTATS `20) and the best known private algorithm, even for the weaker setting when projections are available (Smith-Thakurta, NeurIPS `13).
