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.
The study develops a theory for structured prediction using smooth convex surrogates.
problem Developing a theoretical framework for structured prediction.
method Characterizing smooth convex surrogates compatible with task losses and deriving statistical guarantees.
result Derives tight bounds for the calibration function and novel results for existing surrogate frameworks.
Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classificat…
Algorithm leverages low-rank relations between surrogate tasks for structured prediction.
problem Structured prediction with large or infinite-dimensional surrogate spaces.
method Trace norm regularization to leverage relationships between surrogate outputs without explicit coding/decoding functions.
result Our algorithm can improve generalization performance over previous methods.
Generalizes NTK for surrogate gradient learning in neural networks.
problem Lack of theoretical foundation for surrogate gradient learning.
method Generalizes neural tangent kernel (NTK) for surrogate gradient learning (SGL).
result Surrogate gradient NTK provides a good characterization of SGL.
SURGIN uses generative models to infer subsurface flow data efficiently.
problem Inefficient and task-specific inversion methods for subsurface multiphase flow.
method SURGIN integrates U-FNO surrogate with SGM for zero-shot conditional generation.
result Decent inference of heterogeneous geological fields and flow dynamics with uncertainty quantification.
Optimal sampling strategy improves prediction accuracy with surrogate variables under measurement constraints.
problem Measurement-constrained datasets and lack of labeled data.
method A-optimality criterion for optimal sampling, leveraging surrogate variables.
result Achieves lower asymptotic variance and reduced empirical mean squared error.
A new framework improves reinforcement learning algorithms with policy guarantees.
problem Designing efficient and stable reinforcement learning algorithms.
method A general framework (FMA-PG) based on functional mirror ascent that constructs surrogate functions enabling policy improvement guarantees.
result The proposed framework enables policy improvement guarantees that hold regardless of policy parameterization, and recovers important heuristics.
Study on calibration and consistency of adversarial surrogate losses.
problem Designing robust classifiers with theoretical guarantees.
method Extensive analysis of H-calibration and H-consistency of adversarial surrogate losses.
result Some convex loss functions and supremum-based convex losses are not H-calibrated for important hypothesis sets.
We establish linear regret bounds for convex smooth losses using Fenchel-Young losses.
problem Establishing linear regret bounds for convex smooth losses.
method Constructing a convex smooth surrogate loss using Fenchel-Young losses generated by the convolutional negentropy.
result We derive a smooth loss with a linear surrogate regret bound.
UA-SABI uses surrogates to speed up Bayesian inference for expensive models.
problem Inference for computationally expensive models is slow and uncertain.
method Combines surrogate modeling with Amortized Bayesian Inference (ABI) to propagate uncertainties.
result Reliable, fast, and repeated Bayesian inference for expensive models is achieved.
We present surrogate regret bounds for arbitrary surrogate losses in the context of binary classification with label-dependent costs. Such bounds relate a classifier's risk, assessed with respect to a surrogate loss, to its cost-sensitive classification risk. Two approaches to surrogate regret bounds are developed. The…
Efficient surrogate model reduces ESM evaluation time.
problem Reducing computational time for large-scale ESM simulations.
method SVD for dimensionality reduction followed by Bayesian optimization for neural network surrogate.
result 20 ESM simulations build a 42660-variable surrogate with 93% consistency and 2% MSE.
Proposes a framework for creating custom surrogate explanations.
problem Misunderstanding of LIME as the solution for surrogate explanations.
method Decomposes surrogate explainers into algorithmically independent modules.
result Empowers researchers to create custom local surrogate explanations.
Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However, classical convex surrogates can only tightly bound modular loss functions, sub-modular functions or supermodular functions separately while …
Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ). From local interpolants to global spectral decompositions, surrogates are characterised by their ability to efficiently emulate complex computational models …
Concepts simplify complex models for better understanding.
problem Difficulty in generating understandable explanations for high-dimensional tabular datasets with correlated variables.
method Introduces concepts as groupings of correlated variables and embeds them in a surrogate decision tree.
result Improvement in human interpretability of surrogates without sacrificing accuracy and fidelity.
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.
This work introduces a new metric to assess the fidelity of surrogate models to the underlying data-generating signal.
problem The limitations of fidelity-based explanations in explainable AI.
method Introduces the linearity score λ(f) to quantify the extent of a regression network's linear decodability. result High-fidelity surrogates can underperform compared to simpler models and even linear baselines trained directly on the data.
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.
CCVFM uses coreset to improve generative models by refining residual flows.
problem Generating multimodal distributions from scratch is challenging.
method Augments hierarchical rectified flow with a data-informed source distribution using a coreset.
result CCVFM achieves competitive few-step generation without a learned noise-to-data map.
Deep learning model predicts subsurface flow dynamics.
problem Predicting dynamic subsurface flow in channelized geological systems.
method Residual U-Net and Convolutional LSTM networks trained on pressure and saturation maps.
result Surrogate model accurately predicts pressure, saturation, and well rates for new realizations.
Study on learning to defer with multiple experts using new surrogate losses.
problem Learning to defer with multiple experts in a machine learning context.
method Introducing a new family of surrogate losses for the multiple-expert setting, proving H-consistency bounds, and designing learning algorithms. result Explicit guarantees for new learning to defer algorithms based on minimization of these surrogate losses.
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.
Proposes a method for inference in high-dimensional classification with non-differentiable surrogate losses.
problem Lack of inference procedures for identifying driving factors in high-dimensional classification with non-differentiable surrogate losses.
method Kernel-smoothed decorrelated score and cross-fitted version for hypothesis tests and interval estimators.
result Valid and superior inference methods for high-dimensional classification with non-differentiable surrogate losses.
Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learnin…
In this dissertation, we focus on several important problems in structured prediction. In structured prediction, the label has a rich intrinsic substructure, and the loss varies with respect to the predicted label and the true label pair. Structured SVM is an extension of binary SVM to adapt to such structured tasks. I…
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.
A new beta-VAE based regression model accelerates oilfield optimization studies.
problem Computational expense of full-physics reservoir simulations.
method beta-VAE for interpretable latent space representation, probabilistic dense layers for uncertainty quantification.
result Interpretable latent representation and quantified uncertainty for optimization decisions.
We develop a framework for consistent polyhedral surrogates in classification and prediction.
problem Designing consistent polyhedral surrogates for classification and prediction problems.
method Formalizing and studying embeddings of predictions as points in R^d, assigning original loss values, and convexifying to create surrogates.
result Established a strong connection between embeddings and polyhedral surrogates, providing constructions and proofs of consistency or inconsistency.
NoFAS combines variational inference and adaptive surrogate models for efficient inference of computationally expensive models.
problem Efficient inference of parameters from data with computationally expensive models.
method Variational inference with normalizing flow and adaptive surrogate model training.
result NoFAS reduces computational cost without sacrificing inferential accuracy.
Develops a transparent surrogate model for complex data.
problem Balancing accuracy and transparency in complex decision-making models.
method Partial dependence effects for feature engineering, smart segmentation, and GLM fitting.
result The maidrr GLM closely approximates a black box model and outperforms benchmarks.
New method for testing directed graphs using surrogate data.
problem No established method for statistical testing on directed graphs.
method Define directed graph wide-sense stationary signals, generate surrogates preserving covariance, construct null distributions.
result Feasibility and superiority of new approach over existing methods.
Develops a numerical algorithm for stochastic impulse control using regression surrogates.
problem Optimal impulse control in stochastic processes.
method Generates statistical surrogates for continuation and intervention functions, recursively trained over simulated state trajectories.
result Demonstrates flexibility and extensibility of the numerical scheme through case studies.
Enhances PCE surrogates using transfer learning for expensive simulations.
problem Over-sampling in PCE for expensive forward models.
method Transfer learning from similar tasks to a new task with limited training data.
result Improves scalability and accuracy of PCE surrogates.
We study consistency properties of surrogate loss functions for general multiclass learning problems, defined by a general multiclass loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning pro…
Meta-materials simulation sped up with energy surrogates.
problem Challenging simulation of complex meta-materials due to high-fidelity PDEs.
method Learned component-level surrogates using neural networks to model stored potential energy.
result Surrogates enable accurate macroscopic behavior simulation without full structure simulation.
Framework creates fast, interpretable surrogates for stochastic simulators with unbounded randomness.
problem Creating accurate and fast approximations for stochastic simulators with unbounded randomness.
method Probabilistic surrogate networks that retain structure of reference simulators and enable amortized inference.
result Surrogates accurately model stochastic programs with unbounded random variables and significantly speed up inference.
Polynomial chaos surrogates handle intrinsic noise in stochastic models.
problem Handling intrinsic noise in stochastic models with parametric uncertainty.
method Developed a PCE surrogate on a joint space of intrinsic and parametric uncertainty using Rosenblatt transformations and Karhunen-Loeve expansion.
result Quantified intrinsic noise contribution to model output variance using PCE Sobol indices.
We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-continuous multiclass loss metrics against the worst-case conditional label distributions (the adversarial distributions) that (approximately) m…
Gaussian process models simplify neural network behavior for easier understanding.
problem Understanding and predicting the behavior of deep learning systems.
method Constructing surrogate models using Gaussian processes from finite neural networks.
result Surrogate models capture phenomena like spectral bias and predict generalization well.
This work improves neural likelihood surrogates for stochastic models with a score-augmented loss.
problem Efficient parameter inference for stochastic models with computationally expensive likelihood functions.
method Score-augmented loss function for neural network likelihood surrogates.
result Improves surrogate quality at a lower computational cost compared to generating more data.
Develops Co_SVR for multi-fidelity modeling combining HF and LF models.
problem Combining high-fidelity and low-fidelity models for efficient design.
method Support vector regression with kernel function and heuristic algorithm.
result Co_SVR outperforms other multi-fidelity surrogate models in prediction accuracy.
POAP and pySOT improve surrogate optimization of expensive functions.
problem Optimizing expensive functions with concurrent evaluations.
method Event-driven asynchronous framework for optimization strategies.
result Asynchronous computation offers significant speed-up advantages.
LGV boosts adversarial attacks by improving surrogate models.
problem Improving the transferability of black-box adversarial attacks.
method LGV uses a pretrained surrogate model and multiple weight sets from additional training epochs to generate an effective surrogate ensemble.
result LGV outperforms other test-time transformations by significant margins.
AEGiS optimizes expensive function evaluations asynchronously.
problem Optimizing expensive black-box functions efficiently.
method Asynchronous ε-Greedy Bayesian Optimisation combining greedy search and Thompson sampling.
result AEGiS outperforms existing asynchronous BO methods.
Paper establishes a universal growth rate for smooth surrogate losses in classification.
problem Analyzing growth rates of consistency bounds for various surrogate losses.
method Proves square-root growth rate for smooth margin-based losses; extends to multi-class classification.
result Demonstrates a universal square-root growth rate for smooth comp-sum and constrained losses.
The paper proposes a scalable framework for uncertainty quantification and propagation in surrogate-based Bayesian inference.
problem Uncertainty in surrogate models and its impact on inference and decision-making.
method Bayesian inference methods for surrogate models with measurement data.
result Scalable framework for uncertainty quantification and propagation in surrogate models.