Abstract Large machine learning (ML) models with improved predictions have become widely available in the chemical sciences. Unfortunately, these do not protect privacy necessary within commercial settings, prohibiting use of potentially extremely valuable data by others. Encrypting prediction process can solve this problem double-blind model evaluation and prohibits extraction training or quer...

The Generalized Minimum Residual (GMRES) method is a popular Krylov subspace projection method for solving a nonsymmetric linear system of equations. On modern computers, communication is becoming increasingly expensive compared to arithmetic operations, and a communication-avoiding variant (CA-GMRES) may improve the performance of GMRES. To further enhance the performance of CAGMRES, in this p...

Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, kernel distillation, for kernel matrix approximation. The idea adopts from knowledge distillat...

We derive and investigate a variant of AIC, the Akaike information criterion, for model selection in settings where the observed data is incomplete. Our variant is based on the motivation provided for the PDIO (\predictive divergence for incomplete observation models") criterion of Shimodaira variant diiers from PDIO in its \goodness-of-t" term. Unlike AIC and PDIO, which require the computatio...

Wind generation with an uncertain nature poses many challenges in grid integration and secure operation of power system. One of these operation problems is the unit commitment. Demand Response (DR) can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to the changes in the price of electricity over time. Further, DR can be also d...

The time-recursive computation has been proved as a particularly useful tool in real-time data compression and in transform domain adaptive filtering, with applications in the areas of audio, radar, sonar and video. Unlike the FFT based ones, the time-recursive architectures require only local communication. Also, they are modular and regular, thus they are very appropriate for VLSI implementat...

Background: The witches’ broom disease of lime caused by <span style="font-variant: normal; font-style: normal; font-family: Capitolium...

Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to the pairwise evaluations in kernel methods, the complexity of kernel computation grows as the data size increases; thus the applicability of kernel methods is limited for large scale datasets. Random Fourier Features (RFF) has been proposed to scale the kernel method for solving large scale data...