搜索结果: 1-15 共查到“minimax”相关记录63条 . 查询时间(0.062 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Primal Dual Alternating Proximal Gradient Algorithms for Nonsmooth Nonconvex Minimax Problems with Coupled Linear Constraints
耦合 线性约束 非光滑 非凸极小问题 基本对偶交替 近端梯度算法
2023/4/14
Nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose a primal dual alternating proximal grad...
Minimax Risk: Pinsker Bound
COMMUNICATION THEORY, STATISTICAL DENSITY ESTIMATION FISHER INFORMATION KERNEL ESTIMATORS LINEAR ESTIMATORS, BAYES LOCAL ASYMPTOTIC NORMALITY METHOD OF SIEVES MINIMAX ESTIMATION NOISE (SIGNAL PROCESSING IN THE PRESENCE OF ) PREDICTION AND FILTERING LINEAR SIEVES, METHOD OF SPECTRAL ANALYSIS SHRINKAGE ESTIMATORS SMOOTHNESS PRIORS SOBOLEV SPACES SPLINE FUNCTIONS STATIONARY PROCESSES STEIN EFFECT
2015/8/25
We give an account of the Pinsker bound describing the exact asymptotics of the minimax risk in a class of nonparametric smoothing problems. The parameter spaces are Sobolev classes or ellipsoids, and...
The Asymptotic Minimax Constant for Sup-Norm Loss in Nonparametric Density Estimation
Density estimation exact constant optimal recovery uniform norm risk white noise
2015/8/25
We develop the exact constant of the risk asymptotics in the uniform norm for density estimation. This constant has first been found for nonparametric regression and for signal estimation in Gaussian ...
Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising
Approximate Message Passing Lasso Group Lasso, Joint Sparsity, James- Stein, Minimax Risk over Nearly-Black Objects Minimax Risk of Soft Thresholding Minimax Risk of Firm Thresholding Minimax Shrinkage Nonconvex penalization State Evolution Total Variation Minimization Monotone Regression.
2015/8/21
Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and ...
Consider estimating the mean vector from data Nn(; 2I ) with lq norm loss,
q 1, when is known to lie in an n-dimensional lp ball, p 2 (0; 1). For large
n, the ratio of minimax linear risk to...
On minimax estimation of a sparse normal mean vector
nearly black object robustness white noise model
2015/8/20
Mallows has conjectured that among distributions which are Gaussian but
for occasional contamination by additive noise, the one having least Fisher
information has (two-sided) geometric contaminatio...
Minimax Bayes, asymptotic minimax and sparse wavelet priors
Minimax Decision theory Minimax Bayes estimation
2015/8/20
Pinsker(1980) gave a precise asymptotic evaluation of the minimax mean squared
error of estimation of a signal in Gaussian noise when the signal is known a priori
to lie in a compact ellipsoid in Hi...
We attempt to recover an unknown function from noisy, sampled data. Using
orthonormal bases of compactly supported wavelets we develop a nonlinear method
which works in the wavelet domain by simple ...
Neo-Classical Minimax Problems, Thresholding, and Adaptation
Minimax Estimation Adaptive Estimation
2015/8/20
We study the problem of estimating from data Y N(; 2
) under squared-error loss.
We de ne three new scalar minimax problems in which the risk is weighted by the size of .
Simple thresholding...
A minimax theorem with applications to machine learning, signal processing, and finance
convex optimization minimax theorem robust optimization
2015/8/10
This paper concerns a fractional function of the form x^Ta/sqrt{x^TBx}, where B is positive definite. We consider the game of choosing x from a convex set, to maximize the function, and choosing (a,B)...
A Minimax Theorem with Applications to Machine Learning, Signal Processing, and Finance
convex optimization minimax theorem robust optimization
2015/7/9
This paper concerns a fractional function of the form x^Ta/sqrt{x^TBx}, where B is positive definite. We consider the game of choosing x from a convex set, to maximize the function, and choosing (a,B)...
针对含有未知外部干扰和不确定参数的非线性晶闸管控制串联补偿器(TCSC) 系统, 提出一种L2增益干扰抑制算法. 将minimax 方法引入耗散Hamilton 系统, 消除了不等式假设条件的约束; 构造检验函数, 推算出系统所能承受的最大干扰程度, 降低了传统干扰处理方法的保守性; 采用参数映射方法设计自适应律, 提高了参数跟踪效率. 最后通过机械功率和对地短路故障的仿真结果表明了所提出的控制方...
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
Local Privacy Minimax Bounds Sharp Rates Probability Estimation
2013/6/14
We provide a detailed study of the estimation of probability distributions---discrete and continuous---in a stringent setting in which data is kept private even from the statistician. We give sharp mi...
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Divide and Conquer Kernel Ridge Regression A Distributed Algorithm Minimax Optimal Rates
2013/6/14
We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subse...
Learning subgaussian classes : Upper and minimax bounds
Learning subgaussian classes Upper and minimax bounds
2013/6/14
We obtain sharp oracle inequalities for the empirical risk minimization procedure in the regression model under the assumption that the target $Y$ and the model $\cF$ are subgaussian. The bound we obt...