4 citations · 10 across the 6 of their papers we have counts for
6 papers
Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
Luo Long, Coralia Cartis, Paz Fink Shustin
Bayesian optimisation (BO) enables sample-efficient global optimisation of expensive black-box functions but remains challenging in high dimensions. We investigate nonlinear dimens…
Dimensionality Reduction Techniques for Global Bayesian Optimisation
Luo Long, Coralia Cartis, Paz Fink Shustin
Bayesian Optimisation (BO) is a state-of-the-art global optimisation technique for black-box problems where derivative information is unavailable, and sample efficiency is crucial.…
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
Paz Fink Shustin, Shashanka Ubaru, Małgorzata J. Zimoń +4
Learning data representations under uncertainty is an important task that emerges in numerous scientific computing and data analysis applications. However, uncertainty quantificati…
Semi-Infinite Linear Regression and Its Applications
Paz Fink Shustin, Haim Avron
Finite linear least squares is one of the core problems of numerical linear algebra, with countless applications across science and engineering. Consequently, there is a rich and o…
Gauss-Legendre Features for Gaussian Process Regression
Paz Fink Shustin, Haim Avron
Gaussian processes provide a powerful probabilistic kernel learning framework, which allows learning high quality nonparametric regression models via methods such as Gaussian proce…
Error Inhibiting Schemes for Initial Boundary Value Heat Equation
Adi Ditkowski, Paz Fink Shustin
Finite Difference (FD) schemes are widely used in science and engineering for approximating solutions of partial differential equations (PDEs). Error analysis of FD schemes relies…