Monograph:

• L. Plaskota, "Noisy Information and Computational Complexity", Cambridge University Press, Cambridge, 1996.
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Papers:

• L. Plaskota, and G.W. Wasilkowski, Uniform approximation of piecewise r-smooth and globally continuous functions, submitted.
• L. Plaskota, G.W. Wasilkowski, and Y. Zhao, The power of adaption for approximating functions with singularities, Mathematics of Computation, to appear.
• L. Plaskota, and G.W. Wasilkowski, Adaption allows efficient integration of functions with unknown singularities, Numerische Mathematik 102 (2005), pp. 123-144.
• M.A. Kon, and L. Plaskota, Information-based nonlinear approximation: an average case setting, J. Complexity 21 (2005), pp.211-229.
• L. Plaskota, and G.W. Wasilkowski, Smolyak's algorithm for integration and L₁-approximation of multivariate functions with bounded mixed derivatives of second order, Numerical Algorithms 36 (2004), pp.229-246.
• P. Gajda, Y. Li, L. Plaskota, and G.W. Wasilkowski, A Monte Carlo algorithm for average case weighted integration over R^d, Math. Comp. 73 (2004), pp.813-825.
• L. Plaskota, K. Ritter, and G.W. Wasilkowski, Optimal designs for weighted approximation and integration of stochastic processes on R₊, J. Complexity 20 (2004), pp.108-131.
• L. Plaskota, K. Ritter, and G.W. Wasilkowski, Average case complexity of weighted integration and approximation over R^d with isotropic weight, in Proc. of MCQMC 2002, Hong-Kong 2000, Springer 2002, pp.446-459.
• L. Plaskota, K. Ritter, and G.W. Wasilkowski, Average case complexity of weighted approximation and integration over R₊, J. Complexity 18 (2001), pp.517-544.
• L. Plaskota and G.W. Wasilkowski, The exact exponent of sparse grid quadratures in the weighted case, J. Complexity 17 (2001), pp.840-849.
• M. Kon and L. Plaskota, Complexity of neural network approximation with limited information: a worst case approach, J. Complexity 17 (2001), pp.345-365.
• L. Plaskota, G.W. Wasilkowski, and H. Wozniakowski, A new algorithm and worst case complexity for Feynman-Kac path integration, J. Comput. Physics 164 (2000), pp.335-353.
• M. Kon and L. Plaskota, Information complexity of neural networks, Neural Networks 13 (2000), pp.365-376.
• L. Plaskota, The exponent of discrepancy of sparse grids is at least 2.1933, Advances in Comput. Math. 12 (2000), pp.2-24.
• L. Plaskota, Average case uniform approximation in the presence of Gaussian noise, J. Approx. Theory 93 (1998), pp. 501-515.
• L. Plaskota, Worst case complexity of problems with random information noise, J. Complexity 12 (1996), pp. 416-439.
• L. Plaskota, Survey of computational complexity with noisy information, in ``The Mathematics of Numerical Analysis'', vol. 32 (1996), Proc. of 1995 AMS-SIAM Summer Seminar in Appl. Math., Park City, Utah, ser. Lecture in Appl. Math., eds. J. Renegar, M. Shub, and S. Smale, pp. 651-664.
• L. Plaskota, How to benefit from noise, J. Complexity 12 (1996), pp. 175-184.
• L. Plaskota, Complexity of problems with noisy information, in ``Applied Stochastic and Optimization'', Special Issues of Zeitschrift für Angewandte Mathematik und Mechanik (ZAMM), Issue 3, O. Mohrenholtz, K. Morti, R. Mennicken (eds.), Proc. of ICIAM/JuneGAMM 95 Symposium in Hamburg, Germany, pp. 116-120.
• L. Plaskota, Average complexity for linear problems in a model with varying information noise, J. Complexity 11 (1995), pp. 240-264.
• L. Plaskota, Average case approximation of linear functionals based on information with deterministic noise, J. Computing and Information 4 (1994), pp. 21-39.
• L. Plaskota, A note on varying cardinality in the average case setting, J. Complexity 9 (1993), pp.458-470.
• L. Plaskota, Optimal approximation of linear operators based on noisy data on functionals, J. Approx. Theory 73 (1993), pp.93-105.
• B.Z. Kacewicz and L. Plaskota, The minimal cost of approximating linear operators using perturbed information, J. Complexity 9 (1993), pp.113-134.
• L. Plaskota, Function approximation and integration on the Wiener space with noisy data, J. Complexity 8 (1992), pp.301-323.
• B.Z. Kacewicz and L. Plaskota, Termination conditions for approximating linear problems with noisy information, Math. of Comput. 59 (1992), pp.503-513.
• B.Z. Kacewicz and L. Plaskota, Noisy information for linear problems in the asymptotic setting, J. Complexity 7 (1991), pp.35-57.
• B.Z. Kacewicz and L. Plaskota, On the minimal cost of approximating linear problems based on information with deterministic noise, Numer. Funct. Anal. and Optimiz. 11 (1990), pp.511-528.
• L. Plaskota, On average case complexity of linear problems with noisy information, J. Complexity 6 (1990), pp.199-230.
• L. Plaskota, Asymptotic error for the global maxima of functions in $s$ dimensions, J. Complexity 5 (1989), pp.369-378.
• L. Plaskota, Optimal linear information for the search for the maximum of real functions (in Russian), Zh. Vychisl. Mat. i Mat. Fiz. 26 (1986), pp.934-938.