We present a novel algorithm in order to minimize the regret in an unconstrained action space. Our algorithm hinges on the idea of introducing randomization to ...

We present a novel algorithm in order to minimize the regret in an unconstrained action space. Our algorithm hinges on the idea of introducing randomization to ...

Missing: Minimizing | Show results with:Minimizing

A novel algorithm is presented in order to minimize the regret in an unconstrained action space by introducing randomization to approximate the gradients of ...

Jun 13, 2018 · We present a novel algorithm to minimize regret in unconstrained action spaces. Our algorithm hinges on a classical idea of one-point estimation ...

Therefore, we consider online linear optimization where the goal is to maximize cumulative reward given adversarially selected linear reward functions ft(x) = ...

Missing: Unconstrained | Show results with:Unconstrained

The work in [16] extends these results to zeroth-order methods. Unconstrained distributed online gradient descent algorithms are studied in [17]- [20]. ...

Therefore, we consider online linear optimization where the goal is to maximize cumulative reward given adversarially selected linear reward functions ft(x) = ...

In this chapter we describe the recent framework of online convex optimization which nat- urally merges optimization and regret minimization. We describe the ...