In this paper, we consider online learning with non-convex loss functions. Similar to Besbes et al. [2015] we apply non-stationary regret as the performance ...
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The performance of online learning algorithms is commonly evaluated by regret, which is defined as the difference between the real cumulative loss and the ...
In this paper, we introduce the concept of nonconvexity regret to measure the performance of a local search ... optimal algorithm for online non-convex learning,” ...
Abstract. Motivated by applications in machine learning and operations research, we study regret minimization with stochastic first-order oracle feedback in ...
This paper studies computationally tractable notions of regret minimization and equilibria in non-convex repeated games. Efficient online learning algorithms ...
In many online learning paradigms, convexity plays a central role in the derivation and analysis of online learning algorithms.
Nov 13, 2018 · A typical measure to evaluate online learning algorithms is regret but such standard definition of regret is intractable for nonconvex models ...
We introduce a local regret for non-convex models in a dynamic environment. We present an update rule incurring a cost, according to our proposed local regret, ...
Despite its favorable characteristics, requiring that a function satisfy the PL condi- tion still significantly restricts the type of nonconvex functions that ...
This paper introduces a local regret for non-convex models in a dynamic environment. The authors present an update rule incurring a cost that is sublinear in ...