Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
Meta-models predict model hyperparameters for NDT experiments.
problem Non-destructive testing experiments are isolated; this work connects them.
method Bayesian multilevel approach, capturing inter-task relationships.
result Meta-models encode knowledge between and within tasks for transfer learning.
We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The confidence score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probe…
Meta-model framework improves efficiency in parameter estimation of dynamical systems.
problem Efficiently estimating parameters of complex dynamical systems with computationally expensive objective functions.
method Dynamic adaptation of surrogate model and substitution strategy using a meta-model framework.
result Significant improvement in optimization efficiency, reducing evaluations by up to 77%.
This paper presents an automatic approach for selecting optimal meta-models for sensitivity analysis in complex systems.
problem Efficient surrogate models for high-dimensional problems in virtual prototyping.
method Automatic selection of meta-models, variable space reduction, and advanced sensitivity measures.
result Optimal meta-models and subspace identification for accurate probabilistic analysis.
A machine learning framework simulates complex multibody dynamics systems.
problem Simulating complex multibody dynamics systems accurately and efficiently.
method Employing deep neural networks to generate a data-driven meta-model of multibody systems.
result The meta-model accurately predicts motion data of multibody systems without solving equations of motion.
Automated framework generates mechanical models via deep reinforcement learning.
problem Deriving theoretical-consistent, micro-structural-based traction-separation laws.
method Meta-modeling framework using deep reinforcement learning to form graph edges and maximize model score.
result Automated model generation outperforms existing cohesive models and detects hidden mechanisms.
Study predicts stream turbidity using surrogate data and meta-model.
problem Costly turbidity sensor deployment limits monitoring networks.
method Dynamic regression (ARIMA), LSTM, GAM models; surrogate covariates (rainfall, water level, temperature, solar exposure); meta-model combining strengths of individual models.
result ARIMA and GAM models with all covariates outperform single models; meta-model yields highest accuracy.
RKHSMetaMod estimates complex models' Hoeffding decomposition for sensitivity analysis.
problem Estimating the Hoeffding decomposition of complex models for sensitivity analysis.
method Penalized empirical least-squares minimization with RKHS ridge group sparse optimization.
result Estimates non-zero Sobol indices for sensitivity analysis.
Improved accuracy in dynamic response variation analysis using multi-fidelity data fusion.
problem Inefficient characterization of dynamic response variation due to limited high-fidelity data.
method Composite Neural Network fusion approach for multi-level, heterogeneous datasets.
result Improved accuracy in frequency response variation characterization.
A method to improve gradient boosting models using stacking.
problem Improving the performance of gradient boosting models.
method Proposes a stacking algorithm to learn a meta-model for ensembles of gradient boosting models.
result The proposed approach can be extended to differentiable combination models like neural networks.
Computer simulation has become the standard tool in many engineering fields for designing and optimizing systems, as well as for assessing their reliability. To cope with demanding analysis such as optimization and reliability, surrogate models (a.k.a meta-models) have been increasingly investigated in the last decade.…
HML improves meta learning for diverse tasks.
problem Few-shot learning with heterogeneous tasks.
method Hierarchical factorization and dual-level training.
result HML outperforms existing methods in generalization.
Paper tackles uncertainty prediction for deep sequential regression.
problem Challenges in generating accurate uncertainty estimates for deep recurrent networks.
method Flexible method that generates symmetric and asymmetric uncertainty estimates without stationarity assumptions.
result Outperforms competitive baselines on both drift and non-drift scenarios.
Machine learning models accurately predict maize yield but less so for nitrate loss.
problem Predicting maize yield and nitrate loss for decision-making.
method Evaluation of five machine learning algorithms as meta-models for a cropping systems simulator.
result Random forests most accurately predicted maize yield and nitrate loss.
M3PO improves model-based meta-RL with theoretical guarantees.
problem Improving sample efficiency in multi-task RL with theoretical guarantees.
method Extending Janner et al. (2019) theorems, proposing M3PO with performance guarantees.
result M3PO outperforms existing methods in continuous-control benchmarks.
jinns is a JAX library for physics-informed neural networks.
problem Physics-informed neural networks for forward and inverse problems.
method Physics-informed neural networks using JAX ecosystem.
result Efficient prototyping and extensions for real problems.
The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN architecture for variable flow conditions and object geometry, in addition to identifying a sufficient data preparation process. Multiple CNN …
This paper uses XAI techniques to explain meta-learning models.
problem Lack of understanding how meta-features contribute to model performance.
method XAI techniques applied to explain black-box surrogate models.
result Improved understanding of meta-features' importance and effect.
Gestalt combines two models to improve SQuAD2.0 performance.
problem Improving the accuracy of answering questions in context paragraphs.
method A stacking ensemble of ALBERT and RoBERTa models, combined with a CNN-based meta-model.
result Best ensemble achieved 87.117 EM and 90.306 F1 scores, improving baseline by 0.55% and 0.61% respectively.
Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate computational model (e.g. a finite element model). In this respect simulation methods, whic…
Meta-learning system recommends best multi-target regression method.
problem Improving predictive performance in multi-target regression problems.
method Developed a meta-learning system to recommend the best multi-target regression method for a given problem.
result Meta-models were able to recommend the best method with a balanced accuracy superior to 70%.
ROM-net framework applies to industrial design uncertainty quantification.
problem Uncertainty quantification in industrial design models.
method Dictionary-based ROM-net framework for reduced order modeling.
result ROM-net computes predictions in 2 hours with high accuracy.
New algorithm for online meta-learning with task boundary detection.
problem Adapting to new tasks in a non-stationary environment.
method Two detection mechanisms for task switches and distribution shift; online model updates based on current data.
result Achieves sublinear task-averaged regret under mild conditions.
A new method for efficient probabilistic meta-learning.
problem High-quality predictions with well-calibrated uncertainty estimates require large amounts of data.
method Amortised Inference in Bayesian Neural Networks (APOVI-BNN)
result The APOVI-BNN produces high-quality predictions with well-calibrated uncertainty estimates using significantly less data.
Aggregates models from different datasets using shared latent structures.
problem Aggregating models from heterogeneous datasets with shared latent structures.
method Bayesian nonparametrics for identifying correspondences among local model parameterizations.
result Framework successfully aggregates various model types across different applications.
RocketStack integrates predictions from multiple base learners using a recursive stacking architecture up to ten levels.
problem Feature redundancy, complexity, and computational burden in deep stacking.
method Level-aware recursive stacking with pruning and compression techniques.
result Increasing accuracy with depth and outperforming standalone ensembles at later levels.
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…
In the field of structural reliability, the Monte-Carlo estimator is considered as the reference probability estimator. However, it is still untractable for real engineering cases since it requires a high number of runs of the model. In order to reduce the number of computer experiments, many other approaches known as …
New method for decomposing high-dimensional parametric domains using PCA and inverse projection.
problem Decomposing high-dimensional parametric domains efficiently.
method Iterative Principal Component Analysis (PCA) and inverse projection methods.
result The proposed method effectively reconstructs the original domain from lower-dimensional data.
Meta-learn Bayesian inference for task-specific BNNs using amortised inference.
problem Efficiently learning Bayesian inference for small-scale probabilistic meta-learning.
method Replace global inducing points with actual data to create a set of approximate likelihoods, train a meta-model to learn these parameters across related datasets.
result Meta-learned inference can be applied to task-specific BNNs, improving efficiency and scalability.
Meta learning works well with overparameterized models, a phenomenon called 'benign overfitting'.
problem Understanding why overparameterized models perform well in few-shot learning.
method Analyzed the generalization performance of gradient-based meta learning with an overparameterized meta linear regression model.
result Demonstrated that overparameterized meta learning can still generalize well, a phenomenon called 'benign overfitting'.
New GP kernel handles mixed-categorical data, improving model accuracy.
problem Improving Gaussian process models for mixed-categorical data.
method Extends continuous exponential kernels to handle mixed-categorical variables.
result The proposed GP model gives higher likelihood and smaller residual error.
Human-AI teaming suffers from calibration issues.
problem Human-AI teaming
method Assume calibrated models and humans
result Existing methods for combination do not preserve human's calibration.
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.
Improves algorithm selection for thousands of candidates using dyadic features.
problem Selecting the best algorithm from a large set of candidates for specific problems.
method Proposes extreme algorithm selection (XAS) with dyadic feature representation.
result Improves significantly over current state of the art in various metrics.
Enhances Bayesian model comparison with a probabilistic framework for meta-uncertainty.
problem Uncertainty in posterior model probabilities (PMPs) when derived from finite data.
method Develops a fully probabilistic approach to quantify and represent meta-uncertainty over PMPs.
result Demonstrates utility in various BMC contexts, including regression, MCMC, and neural networks.
Paper investigates privacy-preserving model interpretation in Federated Learning.
problem Balancing model interpretability and data privacy in Federated Learning.
method Uses Shapley values to balance feature importance between host and guest parties in vertical Federated Learning.
result Proposes a method to reveal detailed feature importance for host features and a unified importance value for guest features, maintaining privacy.
MxML combines multiple meta-learners to improve few-shot classification.
problem Few-shot classification performance degrades when a new task is out of the training distribution.
method Train an ensemble of meta-learners (MxML) with mixing parameters optimized by a weight prediction network (WPN).
result MxML significantly outperforms state-of-the-art meta-learners and their naive ensemble.
Meta-learn sparse Gaussian process inference for faster predictions.
problem Cubic computational cost of exact Gaussian process inference for many observations.
method Meta-learn sparse Gaussian process inference.
result Rapid prediction on new tasks with sparse Gaussian processes.
Meta-learning framework improves model performance on few-shot classification tasks.
problem Improving model performance on few-shot classification tasks.
method Empirical Bayes formulation with synthetic gradients for transductive meta-learning.
result Meta-learning framework outperforms previous state-of-the-art methods on benchmarks.
MARCO-GE selects clustering algorithms using graph embeddings.
problem Automated selection of clustering algorithms for unseen datasets.
method Transform datasets into graphs, extract latent representations, train a ranking meta-model.
result MARCO-GE outperforms state-of-the-art approaches in algorithm recommendation.
Proposes efficient user cold start recommendation via meta parameter partition.
problem User cold start in recommendation systems.
method Divides model parameters into fixed and adaptive parts, learning them separately offline and online.
result Significant improvement in AUC (2.48% absolute improvement).
ST-MAML tackles task ambiguity in meta-learning by encoding tasks with stochastic representations.
problem Handling tasks from multiple distributions is challenging for meta-learning due to task ambiguity.
method ST-MAML uses a stochastic neural network module to encode tasks and propagate task representations to revise input variable encoding.
result ST-MAML matches or outperforms state-of-the-art methods on various tasks.
This study presents two new algorithms for solving linear stochastic bandit problems. The proposed methods use an approach from non-parametric statistics called bootstrapping to create confidence bounds. This is achieved without making any assumptions about the distribution of noise in the underlying system. We present…
This paper introduces Sigma, a domain-specific computational representation for collaboration in large-scale for the field of economics. A computational representation is not a programming language or a software platform. A computational representation is a domain-specific representation system based on three specific …
Predictive modelling and supervised learning are central to modern data science. With predictions from an ever-expanding number of supervised black-box strategies - e.g., kernel methods, random forests, deep learning aka neural networks - being employed as a basis for decision making processes, it is crucial to underst…
Paper proposes a new meta-learning approach for correcting noisy labels.
problem Learning with noisy labels in machine learning models.
method Meta-learned instance re-weighting approach extended to label correction problem.
result Proposed MLC (Meta Label Correction) framework achieves large improvements over previous methods.