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Found 12 papers, 4 papers with code
Electricity Price Forecasting in the Irish Balancing Market
We compare statistical, machine learning, and deep learning models using a framework that investigates the impact of different training sizes.
Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning
Results show that modelling the uncertainty of predictions has a positive impact on performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications.
A hybrid estimation of distribution algorithm for joint stratification and sample allocation
In this study we propose a hybrid estimation of distribution algorithm (HEDA) to solve the joint stratification and sample allocation problem.
Combining K-means type algorithms with Hill Climbing for Joint Stratification and Sample Allocation Designs
This is a combinatorial optimisation problem in which we search for the optimal stratification from the set of all possible stratifications of basic strata.
A Simulated Annealing Algorithm for Joint Stratification and Sample Allocation Designs
We add, to the existing suite of local search algorithms, a simulated annealing algorithm that allows for an escape from local minima and uses delta evaluation to exploit the similarity between consecutive solutions, and thereby reduces the evaluation time.
Denoising Dictionary Learning Against Adversarial Perturbations
We show that after applying (DDL) the reconstruction of the original data point from a noisy
Declarative Statistics
In this work we introduce declarative statistics, a suite of declarative modelling tools for statistical analysis.
Stochastic Constraint Programming as Reinforcement Learning
Reinforcement Learning (RL) extends Dynamic Programming to large stochastic problems, but is problem-specific and has no generic solvers.
The BIN_COUNTS Constraint: Filtering and Applications
We show that BIN_COUNTS can be employed to develop a decomposition for the $\chi^2$ test constraint, a new statistical constraint that we introduce in this work.
Statistical Constraints
We introduce statistical constraints, a declarative modelling tool that links statistics and constraint programming.
Piecewise linear approximations of the standard normal first order loss function
When the random variable of interest is normally distributed, the first order loss function can be easily expressed in terms of the standard normal cumulative distribution and probability density function.
Optimization and Control Probability
Confidence-based Reasoning in Stochastic Constraint Programming
In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems.
