minimax-optimal regretWe study the linear contextual bandit problem with finite action sets. When the problem dimension is $d$ , the time horizon is $T$ , and there are $n \\\leq 2^{d/2}$ candidate actions per time period, we 1) show that the minimax expected regret is $\\\Omega...
(2014) Nonconvex statistical optimization: minimax-optimal Sparse PCA in polynomial time. In preparation.Wang, Z., Lu, H., Liu, H., 2014. Tighten after relax: Minimax-optimal Sparse PCA in polynomial time. Adv. Neural Inf. Process. Syst., 3383-3391....
(2001). Minimax Optimal Designs for Nonparametric Regression — A Further Optimality Property of the Uniform Distribution. In: Atkinson, A.C., Hackl, P., Müller, W.G. (eds) mODa 6 — Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Physica, Heidelberg. https://...
In this article, nondegenerate necessary conditions of optimality are derived and discussed for the so-called “minimax” optimal control problems with state constraints. In this class of problems, the data depends on an unknown parameter that takes values in a given compact set called “uncertainty...
This paper is devoted to the problem of minimax optimal control problems of an extensible beam equation with distributed controls and initial velocity disturbances (or noises). The existence of optimal solutions for distributed control with fixed disturbance, namely the problem, and the existence of ...
Minimax-optimal rates for sparse additive models over kernel classes via convex programming Garvesh Raskutti,Martin J. Wainwright,Bin Yu Full-Text Cite this paper Add to My Lib Abstract: Sparse additive models are families of $d$-variate functions that have the additive decomposition $f^* =...
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming. Yu. Minimax-optimal rates for sparse additive models over kernel classes via convex programming. Journal of Machine Learning Research, 12:389-427, March 2012.Garvesh Raskutti,Martin J. Wainwright,Bin Yu.Gar...
These optimizations are the main factor in providing the minimax optimal performance guarantees, especially when observations are stochastically missing. However, in real world scenarios, these properties of the underlying stochastic settings may not be revealed to the optimizer. For such a scenario, ...
E(s2)-optimal and minimax-optimal cyclic supersaturated designs via multi-objective simulated annealing. J. Statist. Plann. Inference, in press, doi: 10.1016/j.jspi.2007.05.044.Koukouvinos C, Mylona K, Simos DE (2008) E ( s 2 )-optimal and minimax-optimal cyclic supersaturated designs via...
In this paper, we propose Bayesian convex and linear aggregation approaches motivated by regression applications. We show that the proposed approach is minimax optimal when the true data-generating model is a convex or linear combination of models in the list. Moreover, the method can adapt to ...