L. Plaskota

发表

Noisy information and computational complexity

Leszek Plaskota, L. Plaskota, 1996 .

Optimal Approximation of Linear Operators Based on Noisy Data on Functionals

L. Plaskota, 1993 .

A new algorithm and worst case complexity for Feynman-Kac path integration

H. Wozniakowski, G. Wasilkowski, L. Plaskota, 2000 .

Complexity of Predictive Neural Networks International Conference on Complex Systems , May , 2000

M. Kon, L. Plaskota, 2000 .

On Linear Versus Nonlinear Approximation in the Average Case Setting

L. Plaskota, 2018 .

Noisy Information: Optimality, Complexity, Tractability

L. Plaskota, 2013 .

ADAPTION MAKES IT EASY TO INTEGRATE FUNCTIONS WITH UNKNOWN SINGULARITIES

G. Wasilkowski, L. Plaskota, 2022 .

On the minimal cost of approximating linear problems based on information with deterministic noise

B. Kacewicz, L. Plaskota, 1990 .

Monte Carlo integration with adaptive variance reduction: an asymptotic analysis

L. Plaskota, Paweł Przybyłowicz, L. Stepien, 2022, ArXiv.

Continuous problems: optimality, complexity, tractability

L. Plaskota, 2014 .

Infinite-dimensional integration and the multivariate decomposition method

I. Sloan, G. Wasilkowski, L. Plaskota, 2015, J. Comput. Appl. Math..

The multivariate decomposition method for infinite-dimensional integration

Grzegorz W. Wasilkowski, Frances Y. Kuo, Dirk Nuyens, 2015 .

Automatic integration using asymptotically optimal adaptive Simpson quadrature

L. Plaskota, 2014, Numerische Mathematik.

Automatic approximation using asymptotically optimal adaptive interpolation

L. Plaskota, Pawel Samoraj, 2021, Numerical Algorithms.

Guest editors' preface

Frances Y. Kuo, Aicke Hinrichs, Klaus Ritter, 2020, Journal of Complexity.

On average case complexity of linear problems with noisy information

Leszek Plaskota, L. Plaskota, 1990, J. Complex..

Worst Case Complexity of Problems with Random Information Noise

Leszek Plaskota, L. Plaskota, 1996, J. Complex..

On Some Integrated Approaches to Inference pdf

Leszek Plaskota, Mark A. Kon, M. Kon, 2012, ArXiv.

Continuous Problems: Optimality, Complexity, Tractability (Invited Talk)

Leszek Plaskota, L. Plaskota, 2014, CASC.

Statistical Likelihood Representations of Prior Knowledge in Machine Learning

Leszek Plaskota, Andrzej W. Przybyszewski, Mark A. Kon, 2005, Artificial Intelligence and Applications.

Machine Learning and Statistical MAP Methods

Leszek Plaskota, Andrzej W. Przybyszewski, Mark A. Kon, 2005, Intelligent Information Systems.

Smolyak's Algorithm for Integration and L1-Approximation of Multivariate Functions with Bounded Mixed Derivatives of Second Order

Grzegorz W. Wasilkowski, Leszek Plaskota, G. Wasilkowski, 2004, Numerical Algorithms.

Complexity of Predictive Neural Networks

Leszek Plaskota, Mark Kon, M. Kon, 2006 .

Information complexity of neural networks

Leszek Plaskota, Mark A. Kon, M. Kon, 2000, Neural Networks.

Noisy information for linear problems in the asymptotic setting

Leszek Plaskota, Boleslaw Z. Kacewicz, B. Kacewicz, 1991, J. Complex..

On alternative quantization for doubly weighted approximation and integration over unbounded domains

G. W. Wasilkowski, F. Pillichshammer, P. Kritzer, 2019, J. Approx. Theory.

On efficient weighted integration via a change of variables

Peter Kritzer, G. W. Wasilkowski, Leszek Plaskota, 2018, Numerische Mathematik.

The power of adaptive algorithms for functions with singularities

Grzegorz W. Wasilkowski, Leszek Plaskota, G. Wasilkowski, 2009 .

The power of adaption for approximating functions with singularities

Grzegorz W. Wasilkowski, Leszek Plaskota, Yaxi Zhao, 2008, Math. Comput..

Adaption allows efficient integration of functions with unknown singularities

Grzegorz W. Wasilkowski, Leszek Plaskota, G. Wasilkowski, 2005, Numerische Mathematik.

Complexity of Neural Network Approximation with Limited Information: A Worst Case Approach

Leszek Plaskota, Mark A. Kon, M. Kon, 2001, J. Complex..

New averaging technique for approximating weighted integrals

Grzegorz W. Wasilkowski, Leszek Plaskota, Yaxi Zhao, 2009, J. Complex..

Optimal designs for weighted approximation and integration of stochastic processes on [0, infinity)

Grzegorz W. Wasilkowski, Klaus Ritter, Leszek Plaskota, 2004, J. Complex..

Optimal Designs for Weighted Approximation and Integration of Stochastic Processes on [0;1)

K. Ritter, L. Plaskota, Grzegorz W. WasilkowskiAugust, 2002 .

Function approximation and integration on the wiener space with noisy data

Leszek Plaskota, L. Plaskota, 1992, J. Complex..

Efficient algorithms for multivariate and ∞-variate integration with exponential weight

G. Wasilkowski, L. Plaskota, 2014, Numerical Algorithms.

A Monte Carlo algorithm for weighted integration over Reald

Grzegorz W. Wasilkowski, Youming Li, Leszek Plaskota, 2003, Math. Comput..

Monte Carlo and Quasi-Monte Carlo methods 2010

Leszek Plaskota, Henryk Woźniakowski, H. Wozniakowski, 2012 .

Average Case Complexity of Weighted Approximation and Integration over R+

Grzegorz W. Wasilkowski, Klaus Ritter, Leszek Plaskota, 2002, J. Complex..

Complexity of approximating Hölder classes from information with varying Gaussian noise

Leszek Plaskota, Paweł Morkisz, Pawel M. Morkisz, 2020, J. Complex..

Average CaseL∞-Approximation in the Presence of Gaussian Noise

L. Plaskota, 1998 .

Monte Carlo integration of Cr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C^r$$\end{document} functions with adapti

L. Plaskota, Paweł Przybyłowicz, Łukasz Stȩpień, 2022, BIT Numerical Mathematics.