Bandit convex optimization algorithm

  • How do you solve non-convex optimization problems?

    Strategy 1: local optimization of the non-convex function All convex functions rates apply. ○ rescales gradients by the absolute value of the inverse Hessian and the Hessian's Lanczos vectors. ○ matrix completion ○ Image reconstruction ○ recommendation systems..

Bandit convex optimization (BCO) is a key framework for modeling learning problems with sequential data under partial feedback. In the BCO scenario, at each round, the learner selects a point (or action) in a bounded convex set and observes the value at that point of a convex loss function determined by an adversary.
Bandit convex optimization (BCO) is a key framework for modeling learning problems with sequential data under partial feedback. In the BCO scenario, at each round, the learner selects a point (or action) in a bounded convex set and observes the value at that point of a convex loss function determined by an adversary.

What are kernel-based methods for bandit convex optimization algorithm with -regret?

Kernel-based methods for bandit convex optimization algorithm with -regret for this problem

To do so we introduce three new ideas in the derivative-free optimization literature: (i) kernel methods, (ii) a generalization of Bernoulli convolutions, and (iii) a new annealing schedule for exponential weights (with increasing O(n9

5√ T)-regret,

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