A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study evaluates the importance of design of experiments for PINN in physics-informed deep learning.
Design of experiments improves validation of biomolecular networks.
Neural Optimal Design of Experiments improves inverse problem solving efficiency.
We introduce an application of the group lasso to design of experiments. Note that we are NOT trying to explain experimental design for the group lasso. Conversely, we explain how we can use the idea of the group lasso in experimental design, showing that the problem of constructing an optimal design matrix can be tran…
Adapts MBDOE for real-time parameter estimation in complex systems.
Green LIME reduces LIME's computational cost through optimal design of experiments.
A method to select validation data from a dataset using statistical criteria.
Do-AIQ framework evaluates AI algorithms' quality using DOE.
Bayesian SDOE method estimates QoIs from expensive black-box functions efficiently.
We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling (MPS), is inspired by the classical posterior (Thompson) sampling algorithm for multi-armed…
Algorithm improves reinforcement learning policies using offline data.
Healthcare companies must submit pharmaceutical drugs or medical devices to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biolog…
BoFire optimizes chemistry experiments using Bayesian Optimization.
Model discrimination identifies a mathematical model that usefully explains and predicts a given system's behaviour. Researchers will often have several models, i.e. hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. discard inaccurate models. G…
Optimizes biomanufacturing processes with a new digital twin calibration method.
Unsupervised machine learning helps design complex experiments more efficiently.
In engineering applications almost all processes are described with the help of models. Especially forming machines heavily rely on mathematical models for control and condition monitoring. Inaccuracies during the modeling, manufacturing and assembly of these machines induce model uncertainty which impairs the controll…
New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.
The paper investigates AI robustness through experiments and statistical analysis.
Bayesian design improves experimental optimization.
We consider the problem of estimating the set of all inputs that leads a system to some particular behavior. The system is modeled by an expensive-to-evaluate function, such as a computer experiment, and we are interested in its excursion set, i.e. the set of points where the function takes values above or below some p…
Bayesian optimal design of experiments (BODE) has been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE method…
We present a simulation framework for spunbond processes and use a design of experiments to investigate the cause-and-effect-relations of process and material parameters onto the fiber laydown on a conveyor belt. The virtual experiments are analyzed by a blocked neural network. This forms the basis for the prediction o…
Optimizes sampling in continuous domains by adjusting search distribution.
We investigate two new strategies for the numerical solution of optimal stopping problems within the Regression Monte Carlo (RMC) framework of Longstaff and Schwartz. First, we propose the use of stochastic kriging (Gaussian process) meta-models for fitting the continuation value. Kriging offers a flexible, nonparametr…
This work uses RL to optimize batch experiments in SDOE.
Characterizing the appearance of real-world surfaces is a fundamental problem in multidimensional reflectometry, computer vision and computer graphics. For many applications, appearance is sufficiently well characterized by the bidirectional reflectance distribution function (BRDF). We treat BRDF measurements as sample…
Designs a single policy for collecting data to train near-optimal policies.
Proposes a method to generate multivariate prediction intervals for random forests.
Study efficient resource allocation for detecting extreme values.
Paper proposes online learning for estimating AC network admittance matrix.
Regression models are increasingly built using datasets which do not follow a design of experiment. Instead, the data is e.g. gathered by an automated monitoring of a technical system. As a consequence, already the input data represents phenomena of the system and violates statistical assumptions of distributions. The …
This article reviews and explains HMC-based methods for sampling constrained continuous distributions.
We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential des…
Predicts optimal training dataset sizes per class for machine learning models.
New algorithm selects optimal subset for multiclass classifier training.
We consider the problem of how to assign treatment in a randomized experiment, in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. …
Paper uses transfer learning and Bayesian optimization to reduce DNA sequence design experiments.
The study examines how experimental design choices affect machine learning model performance.
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian process is a changing task due to the fact that not only we seek to estimate the next samplin…
New algorithms speed up learning from large screens of proteins.
Unified Skew-Gaussian process framework for various regression and classification tasks.
Study designs experiments to identify causal graph structure with cycles and latent confounders.
Bayesian optimization speeds up bioprocess development across scales.
In this article, we consider a stochastic numerical simulator to assess the impact of some factors on a phenomenon. The simulator is seen as a black box with inputs and outputs. The quality of a simulation, hereafter referred to as fidelity, is assumed to be tunable by means of an additional input of the simulator (e.g…
Bayesian DOE accelerates experimental design with improved efficiency.
CRPS improves GP-based sequential design for chemical space.
The inverse statistical problem of finding direct interactions in complex networks is difficult. In the natural sciences, well-controlled perturbation experiments are widely used to probe the structure of complex networks. However, our understanding of how and why perturbations aid inference remains heuristic, and we l…