Improved machine learning model performance through data augmentation, custom loss functions, and transfer learning.
problem Poor performance of a traditional engineering model due to limited training data.
method Data augmentation, custom loss functions, transfer learning.
result Improvement of at least 38% in performance across five models.
A machine learning surrogate model predicts earthquake-induced building responses.
problem Expensive FE model simulations for earthquake damage estimation.
method SVD-based earthquake characterization and machine learning model training.
result Deep neural network provides most accurate predictions of building responses.
New method integrates real and synthetic data to improve machine learning models.
problem Expensive or impractical collection of high-quality data limits machine learning.
method Weighted empirical risk minimization approach for integrating surrogate data.
result Integrating surrogate data can significantly reduce test error on the original distribution.
The paper studies consistency of surrogate loss procedures under constrained classifiers.
problem Consistency of surrogate loss approaches under constrained classifiers without correct specification.
method The paper develops theoretical results and hinge loss based procedures for a constrained classification problem.
result Hinge losses are the only surrogate losses that preserve consistency in second-best scenarios.
This paper develops nudging algorithms using learned surrogates for state estimation in dynamical systems.
problem Estimating the state of a dynamical system from partial observations when dynamics are unknown or expensive to simulate.
method Unified finite-dimensional analysis of nudging algorithms employing learned surrogate models of the dynamics.
result Nudging algorithms with surrogate models retain exponential convergence up to an explicit error floor.
New method uses AI predictions as cheaper alternatives to expensive outcomes.
problem Using expensive outcomes for statistical inference.
method Recalibrated prediction-powered inference using machine learning techniques.
result Significant gains in effective sample size over existing PPI proposals.
A new training method uses multilevel minimization for machine learning.
problem Training machine learning models with high variance and low efficiency.
method Constructs a multilevel hierarchy by reducing sample size and internally trains surrogate models with fewer samples.
result The multilevel method enhances model training efficiency compared to subsampled Newton's and variance reduction methods.
New algorithm uses machine learning to predict rewards for decision-making problems.
problem Sequential decision-making under uncertainty with scarce online data.
method Machine Learning-Assisted Upper Confidence Bound (MLA-UCB) algorithm.
result Proves to improve cumulative regret even with biased surrogate rewards.
This work improves surrogate models using low-fidelity data to enhance accuracy and efficiency.
problem Limited training data makes high-fidelity models unreliable.
method Uses low-fidelity data to augment input space and condition high-fidelity models.
result Increased predictive accuracy and reduced computational cost compared to existing methods.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Study on H-consistency bounds for machine learning surrogates.
problem Estimating target loss error relative to surrogate loss error in machine learning.
method Developed H-consistency bounds for various surrogates and loss functions. result Stronger guarantees than existing methods, offering distribution-dependent and -independent bounds.
The paper proposes a method to assess surrogate heterogeneity in non-randomized data.
problem Lack of methods to evaluate surrogate heterogeneity in non-randomized data.
method Proposes a framework using meta-learners to assess surrogate heterogeneity in real-world data.
result Identifies individuals for whom the surrogate is a valid replacement of the primary outcome.
A new method combines SciML and UQ with physical constraints.
problem Uncertainty quantification in scientific machine learning tasks.
method Physics-constrained polynomial chaos expansion.
result Effective uncertainty quantification and SciML integration.
New bounds show polyhedral surrogates are optimal for generalization.
problem Proving generalization rates for polyhedral loss functions.
method Developed two general results for polyhedral surrogates.
result Polyhedral surrogates provide linear surrogate regret bounds, translating directly to target rates.
Symmetric losses improve classifier robustness from corrupted labels.
problem Improving classifier performance from corrupted labels.
method Symmetric losses that satisfy a certain condition.
result Symmetric losses enhance robust classification from corrupted labels.
We study consistency properties of machine learning methods based on minimizing convex surrogates. We extend the recent framework of Osokin et al. (2017) for the quantitative analysis of consistency properties to the case of inconsistent surrogates. Our key technical contribution consists in a new lower bound on the ca…
Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancements in sensor technologies, certain variables cannot be easily and/or economically monitored in-situ and in real time. In these cases, surrogate measures can be used to make estimations by me…
GUESS improves surrogate model accuracy with adaptive sampling.
problem Creating accurate surrogate models with limited data.
method Gradient and Uncertainty Enhanced Sequential Sampling (GUESS) using predictive uncertainty and Taylor expansion.
result GUESS achieved highest sample efficiency compared to other strategies.
Hyperboost uses gradient boosting for hyperparameter optimization, outperforming state-of-the-art methods.
problem Hyperparameter tuning for machine learning algorithms
method Gradient boosting surrogate model with quantile regression and distance metric
result Hyperboost outperforms state-of-the-art techniques in empirical tests
Improving predictive understanding of Earth system variability and change requires data-model integration. Efficient data-model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables i…
Surrogate-based analysis of interactions via local effect smooths
problem Detecting and characterizing feature interactions in machine learning models
method Surrogate-based analysis using generalized additive models
result Empirical validation of effectiveness for pairwise interactions
Local surrogate explainers vary in objectives, leading to incomparable explanations.
problem Variability in objectives among local surrogate explainers.
method Review of multiple local surrogate explainers, focusing on extracted information.
result Diverse explanations from similar methods due to differing objectives.
Paper analyzes proper losses and their performance in machine learning tasks.
problem Understanding the performance of estimators and forecasters in machine learning tasks.
method Analyzes surrogate regret and convergence rates for strictly proper losses.
result Strongly proper losses achieve the optimal convergence rate.
The minimization of loss functions is the heart and soul of Machine Learning. In this paper, we propose an off-the-shelf optimization approach that can minimize virtually any non-differentiable and non-decomposable loss function (e.g. Miss-classification Rate, AUC, F1, Jaccard Index, Mathew Correlation Coefficient, etc…
Paper bounds convergence rate of adversarial surrogate risk.
problem Vulnerability of binary classification models to adversarial attacks.
method Characterizes conditions for adversarial consistency and provides surrogate risk bounds.
result Surrogate risk bounds quantify the rate of convergence of adversarial classification risk.
Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points. The objective is to produce a classifier of relatively low risk, as measured under the 0-1 loss, ideally usin…
Machine learning improves model forecasts by correcting errors.
problem Improving short- to mid-range forecasts by correcting model errors.
method Iterative method combining data assimilation and machine learning.
result Hybrid models outperform original models in forecasts.
We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. To overcome the problem that both treatment and control outcomes for the same unit are required for supervised learning, we propose surrogate loss functions that inco…
Bayesian adaptive PCE method improves surrogate modeling and sensitivity analysis.
problem Lack of fully Bayesian PCE methods in statistics.
method Developed a novel fully Bayesian adaptive PCE method with R implementation.
result Bayesian adaptive PCE provides competitive performance for various UQ tasks.
New method combines machine learning with data assimilation for model error correction.
problem Correcting model errors using sparse and noisy observations.
method Hybrid machine learning and data assimilation methods.
result Tendency correction outperforms resolvent correction in data assimilation experiments.
Estimates long-term effects of new treatments using historical and short-term data.
problem Estimating long-term effects of novel treatments with limited historical data.
method Surrogate indices, dynamic treatment effect estimation, and double machine learning combined in a unified pipeline.
result Consistent and asymptotically normal estimates of long-term effects under Markovian assumption.
Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type of data (images, text and tabular), are model-agnostic and are post-hoc (i.e., can be retrofitted). The Local Interpretable Model-agnostic …
A hybrid framework uses machine learning to price options faster and more accurately.
problem Rapid recalibration of option pricing models in dynamic markets.
method Integrates smooth offset algorithm with supervised machine learning models.
result Surrogate pricing operators achieve up to 1000x speedup over direct SOA evaluation.
This paper explores online learning of dynamics and state using ensemble Kalman filters.
problem Reconstructing dynamics from partial and noisy observations in real-time.
method Ensemble Kalman filter (EnKF) family of algorithms for online learning of dynamics and state.
result Demonstrates the efficiency and accuracy of online learning methods using Lorenz models.
SCaSML improves PDE solvers by correcting errors efficiently.
problem Reliable and error-free high-dimensional PDE solutions.
method Defect correction method to derive a Structural-preserving Law of Defect.
result SCaSML achieves faster convergence and reduced errors in high-dimensional PDEs.
Generative AI improves surrogate models by blending LF and HF data.
problem Data scarcity between high-fidelity and low-fidelity simulations.
method Probabilistic multi-fidelity surrogate framework using generative transfer learning.
result The model achieves HF accuracy with fewer HF evaluations.
Paper shows SVM can achieve super fast convergence rates.
problem Understanding fast convergence rates for SVM.
method Presented a simple mechanism to obtain fast convergence rates for SVM.
result SVM can exhibit exponential convergence rates without hard Tsybakov margin condition.
PANIS learns PDE surrogates for heterogeneous materials without solving the PDE.
problem Learning surrogates for parametrized PDEs in heterogeneous media.
method Physics-aware neural implicit solvers combining probabilistic learning and physics-informed discretization.
result Learned surrogates for effective solutions in heterogeneous materials without solving the reference problem.
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.
The paper proposes a method to model non-smooth functions using clustering, classification, and Gaussian process modeling.
problem Modeling discontinuities and non-smoothness in expensive computational models.
method Three-stage approach combining clustering, classification, and Gaussian process modeling.
result The approach successfully models discontinuities and non-smoothness in various functions.
Study uses machine learning to optimize stock trading strategies.
problem Optimizing stock trading strategies with machine learning.
method Dynamic programming and deep learning for nonlinear price impact.
result NN surrogates accurately approximate optimal strategies.
Bayesian deep learning improves building energy simulation accuracy.
problem Uncertainty in surrogate models for building energy performance.
method Training dropout neural networks and stochastic variational Gaussian Processes.
result Surrogate models reduce errors by up to 30% with uncertainty-aware sampling.
SurvBeX explains ML survival models using Beran estimator.
problem Interpreting predictions of machine learning survival models.
method Uses modified Beran estimator as surrogate model to compute feature impacts.
result SurvBeX minimizes mean distance between black-box and surrogate model survival functions.
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute t…
A new sampling strategy improves reliability and robustness optimization for complex designs.
problem High sample requirements for optimizing reliability and robustness in complex designs.
method Local Latin Hypercube Refinement (LoLHR) for multi-objective design uncertainty optimization.
result LoLHR achieves better results compared to other surrogate-based strategies.
OMLT combines ML and optimization for solving complex problems.
problem Solving complex decision-making problems in computer science and engineering.
method OMLT integrates neural networks and gradient-boosted trees into optimization problems using machine learning.
result OMLT seamlessly integrates with Pyomo and solves real-world problems.
Proposes MamBO for efficient high-dimensional large-scale optimization.
problem High-dimensional and large-scale optimization problems in machine learning and simulation.
method Combines subsampling and subspace embeddings with model aggregation to address uncertainty in surrogate models.
result Improves robustness of Bayesian optimization algorithm and achieves superior performance.
New method uses imperfect LLM annotations for valid statistical inference in social science.
problem Inaccurate large language model annotations in social science research.
method Design-based supervised learning (DSL) combining imperfect LLM surrogates with gold-standard labels.
result DSL provides valid statistical inference with comparable predictive accuracy to existing methods.