WebThus we satisfy the requirements in (5) and (6), and the minimax lower bound (7) holds. In particular, we have inf f^ sup f2F E h f^ f 3 1 i 1 2 ˙2 n 1: Note that this tight lower bound … Web20 mei 2024 · The hardness of the cost-sensitive classification problem is investigated by extending the standard minimax lower bound of balanced binary classification …
Minimax Bounds for Distributed Logistic Regression
WebWith this theorem, the problem of lower bounding the minimax risk reduces to bounding the average test error on the RHS above. To this send, we will draw techniques from … Web31 okt. 2024 · A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression ... The proof techniques utilized in this work also set the stage for development of … marty mcfly nikes back to the future 1
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs …
Webknowledge, this is the first minimax result on the sample complexity of RL: the upper bounds match the lower bound in terms of N, ε, δ and 1/(1 −γ)up to a constant factor. Also, both our lower bound and upper bound improve on the state-of-the-art in terms of their depen-dence on 1/(1 −γ). Web1 nov. 1999 · This paper proves minimax lower bounds for this problem and explains how can these rates be attained, using in particular an Empirical Risk Minimizer (ERM) … WebLipschitz classes. In Efromovich and Low (1994) a general theory was developed for the case of nested convex parameter spaces. A general extension of this adaptive … marty mcfly outfits