ROD reconstructs conditional mean surfaces from observational data, invariant to term order.
problem Regression estimates from observational data can be biased by multicollinearity and term order.
method Retrospective Orthogonal Design (ROD) reconstructs surfaces on a probability-balanced lattice, preserving observed and completing unsupported cells.
result ROD outperformed polynomial regression across various data-generating processes, achieving high out-of-sample R2. We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given L≥2 response surfaces over a continuous input space X, the aim is to efficiently find the index of the minimal response across the entire X. The response surfaces are not known and ha…
This work accelerates uncertainty propagation in DNNs using active subspace.
problem Challenges in propagating uncertainty in DNN predictions from real-world data.
method Gradient-based active subspace method and response surface technique.
result Efficient uncertainty propagation in DNN predictions at low computational cost.
Deep learning ranks response surfaces for optimal stopping problems in finance.
problem Ranking response surfaces in stochastic control problems.
method Reformulate as image segmentation problem and apply deep learning algorithms.
result Deep learning provides an efficient method for solving optimal stopping problems.
BO optimized for high dimensions using feature space learning.
problem Scalability of BO to high-dimensional problems.
method Joint learning of low-dimensional feature space and reconstruction mapping.
result Optimization in lower-dimensional subspace simplifies BO problem.
Study optimizes CANN for actuarial tasks using RSM.
problem Optimizing hyperparameters for neural networks in actuarial science.
method Factorial design and response surface methodology (RSM).
result Reduced hyperparameter optimization from 288 to 188, achieving near-optimal performance.
Aims to optimize complex multivariate systems with constraints.
problem Optimizing force-field systems in physics with large-scale simulations.
method Combines machine learning and experimental design to find feasible input combinations.
result Locates multiple good regions in the input space.
RLGP model improves robustness and accuracy for discontinuous response surfaces.
problem Challenges in modeling abrupt jumps and discontinuities in response surfaces.
method Integrates adaptive nearest-neighbor selection with robustification mechanism.
result Consistently delivers high predictive accuracy and robustness in higher dimensions.
A new framework interprets machine learning models using Gaussian processes.
problem Interpreting complex machine learning models.
method Gaussian process metamodeling to capture response surfaces.
result Maximizing likelihood function for variable importance.
HierGP improves emulator efficiency for sparse, structured data.
problem Sparse, structured data in expensive simulations.
method Hierarchical shrinkage GP framework with cumulative shrinkage priors.
result HierGP identifies structured sparse features from limited data.
PARyOpt is a python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive for computational optimization due to its low cost function footprint as well as the ability to account for uncertainties in data. A key…
LOOCV is often useful for analyzing small, structured experimental designs.
problem The effectiveness of cross-validation in analyzing designed experiments.
method Empirical study comparing LOOCV and other model selection methods.
result LOOCV is often useful in the analysis of small, structured experimental designs.
We are focusing on bound constrained global optimization problems, whose objective functions are computationally expensive black-box functions and have multiple local minima. The recently popular Metric Stochastic Response Surface (MSRS) algorithm proposed by \cite{Regis2007SRBF} based on adaptive or sequential learnin…
A new method for Gaussian Processes handles mixed continuous and categorical inputs.
problem Modeling cross-correlations between continuous and categorical data.
method Low-Rank Correlation (LRC) method for Gaussian Processes with flexible rank approximation.
result LRC outperforms existing methods in estimating cross-correlations and predicting response surfaces.
New algorithm tackles high-dimensional simulation optimization, converging efficiently.
problem High-dimensional simulation optimization challenges.
method Sparse grid experimental design combined with kernel ridge regression using Brownian field kernel, followed by expected improvement strategy.
result Established upper bounds on convergence rate, demonstrating superior performance in practice.
Neural network with data augmentation improves multi-stage pump prediction accuracy.
problem Predicting multi-stage pump external characteristics with high accuracy.
method Neural network model with data augmentation for multi-objective prediction.
result Neural network model with data augmentation outperforms other models in accuracy.
PSO improves G-optimal designs for up to 5 factors, reducing computation time.
problem Computing highly G-optimal designs for response surface models is computationally expensive. method Extended Particle Swarm Optimization (PSO) for optimal design problems.
result PSO generates improved G-optimal designs for up to 5 factors with comparable computational cost. Bayesian optimisation tackles expensive black-box functions with constraints.
problem Optimizing constrained black-box functions in machine learning and simulation.
method Proposes a new Knowledge Gradient acquisition function for constrained Bayesian optimisation.
result Demonstrates superior performance over four state-of-the-art constrained Bayesian optimisation algorithms.
Bayesian optimization uses shared latent variables for multiple systems.
problem Optimizing systems with limited data and unknown relationships.
method Shared latent variables, Bayesian inference, probabilistic metamodel.
result Performance improvement in zero-, one-, and few-shot settings.
New optimization algorithm for mixed-variable problems improves efficiency.
problem Optimizing functions with both continuous and categorical variables.
method Combines radial basis function and metric stochastic response surface methods with modifications for categorical variables and parallel processing.
result Numerical experiments show the effectiveness of the proposed modifications.
Proposes a model to interpret complex ML algorithms.
problem Complex ML models are hard to interpret.
method Uses model-based regression trees and interpretable main-effects models.
result Surrogate model provides interpretable results with good predictive performance.
An axiomatic approach to signal reconstruction is formulated, involving a sample consistent set and a guiding set, describing desired reconstructions. New frame-less reconstruction methods are proposed, based on a novel concept of a reconstruction set, defined as a shortest pathway between the sample consistent set and…
A new adaptive kriging method improves binary classification of mechanical problems.
problem Efficient binary classification of mechanical problems with high fluctuation.
method Monte Carlo-intersite Voronoi (MiVor) adaptive scheme for regression surrogate model.
result The MiVor algorithm provides accurate binary classification with fewer observation points for highly fluctuating response surfaces.
XPDNet wins MRI reconstruction challenge with neural network.
problem MRI reconstruction from under-sampled data.
method Inspired by MRI and computer vision best practices, XPDNet uses a neural network.
result XPDNet achieved state-of-the-art results in the 2020 fastMRI challenge.
CoRAS adapts image acquisition rates for accurate reconstruction.
problem Determining when enough measurements are collected for accurate image reconstruction.
method Adaptive acquisition rate selection based on reconstruction error probability.
result CoRAS achieves target stopping-time coverage with fewer measurements.
New methods improve accuracy in predicting complex systems.
problem Improving accuracy in predicting complex physical systems from simulators.
method Proposes two new methods of design approaches that sequentially select input settings.
result Demonstrates effectiveness of the proposed methods through numerical examples.
Paper proposes a new method for efficient hyperparameter optimization.
problem Challenging task of optimizing hyperparameters in machine learning.
method Sequential Uniform Design (SeqUD) strategy for adaptive and efficient exploration of hyperparameter space.
result The proposed SeqUD strategy outperforms existing methods in hyperparameter optimization.
We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically using a Bayesian treatment. In this paper, we address the framewor…
New method reconstructs moving parts of proteins in cryo-EM.
problem Reconstructing non-rigid molecules with moving parts in cryo-EM.
method Graph Laplacian construction from multiple projection images, followed by spectral volume expansion.
result High-resolution visualization of molecular dynamics using spectral volumes.
In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude spectrograms do not contain phase information, we must restore or infer phase information to reconstruct a time-domain signal. One widely used approach for dealing w…
The paper discusses algorithms for reconstructing curves with given Euclidean or affine curvatures.
problem Reconstructing planar curves with specified Euclidean or affine curvatures.
method The paper presents algorithms for curve reconstruction under the special Euclidean and equi-affine groups.
result The reconstructed curves are close to the original curves in terms of the specified curvatures.
IAGAN method improves medical image reconstruction by incorporating adaptive GAN priors.
problem Reconstructing high-fidelity medical images from incomplete data.
method Image-adaptive GAN-based reconstruction method (IAGAN).
result IAGAN can recover fine structures relevant for medical diagnosis.
Algorithm reconstructs conserved networks from flow data.
problem Network reconstruction from flow data.
method Polynomial time algorithm exploiting graph theoretic properties and learning techniques.
result Exact network reconstruction possible for arborescence networks.
Paper addresses data reconstruction from privacy-protected templates using STCA.
problem Reconstructing privacy-sensitive data from protected templates.
method Sparse ternary coding with ambiguization (STCA) for privacy preservation.
result STCA maintains theoretical performance against deep reconstruction attacks for synthetic data but requires special measures for real images.
Sparse elasticity reconstruction from local displacements reduces error.
problem Reconstructing elasticity from limited data.
method Sparse elasticity reconstruction theory, local clustering, alternating optimization.
result Higher spatial resolution elasticity distribution estimation.
This work improves understanding of neural network reconstruction attacks and distillation.
problem Understanding and mitigating reconstruction attacks on neural networks.
method Developed a stronger dataset reconstruction attack and studied its characteristics.
result Reconstruction attacks can recover entire training sets in the infinite width regime.
GCVAE improves disentanglement in VAEs while balancing reconstruction error.
problem Improving disentanglement in VAEs while maintaining low reconstruction error.
method Introduces three controllable Lagrangian hyperparameters to optimize reconstruction and KL divergence loss.
result GCVAE outperforms state-of-the-art models in disentanglement while balancing reconstruction.
PC-RNN reconstructs MRI images from undersampled data with more details.
problem Recovering fine details from undersampled MRI data.
method Pyramid Convolutional RNN (PC-RNN) with three ConvRNN modules for multi-scale reconstruction.
result PC-RNN outperforms other methods in recovering more details from MRI images.
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
This work improves data reconstruction methods by ensuring unique solutions and refining optimization.
problem Ensuring unique solutions and optimizing reconstruction from KKT conditions.
method Discussion of sufficient conditions for unique solutions and introduction of sample splitting for optimization.
result Sample splitting improves reconstruction performance across various methods.
New condition for reconstructing Morse functions on 3D manifolds.
problem Reconstructing Morse functions with specific level sets.
method Studied a necessary and sufficient condition for reconstruction.
result New condition strengthens previous sufficient conditions.
DCAE learns compact latent representations for one-class novelty detection.
problem Learning compact latent representations for one-class novelty detection.
method DCAE learns compact and collapse-free latent representations through internal discriminative layers of GANs, reconstructing in-class data finely and exclusively.
result DCAE achieves state-of-the-art performance on novelty and adversarial example detection.
A hybrid network improves MRI reconstruction from undersampled data.
problem Reducing MRI acquisition time while maintaining image quality.
method Hybrid architecture combining k-space and image domains using complex-valued and real-valued U-nets.
result Hybrid approach outperformed image-only deep learning methods in hard-to-reconstruct regions.
Noise2Filter improves 3D tomography reconstruction efficiency and accuracy.
problem Efficiently reconstructing 3D tomographic images in real-time with limited data.
method Self-supervised learning and a learned filter method.
result Noise2Filter achieves real-time reconstruction with limited loss of accuracy.
Single linear solve combines surface reconstruction and uncertainty quantification.
problem Reconstructing surfaces from partial point clouds with uncertainty.
method Geometric Gaussian processes for stochastic surface reconstruction.
result Single linear solve for surface reconstruction with probabilistic capabilities.
Unified theory explains and mitigates double descent in data reconstruction.
problem Understanding and mitigating double descent in reduced order modeling.
method Data-Noise Averaging theory, sufficient criteria, detailed risk curve prediction, regularization mechanisms.
result Detailed risk curves predicted at reduced computational cost, instability traced to individual sensors.
Paper tackles model vulnerabilities by reconstructing training data.
problem Reconstructing training data from model parameters poses a security risk.
method Developed a mathematical framework and score matching method for both Bayesian and non-Bayesian models.
result First score matching framework for reconstructing data in Bayesian models.
DEER network improves few-view breast CT image reconstruction efficiency and quality.
problem Efficient and high-quality few-view breast CT image reconstruction.
method Deep Efficient End-to-end Reconstruction (DEER) network with low model complexity.
result DEER network achieves competitive image quality with significantly fewer parameters compared to state-of-the-art methods.