Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning

• URL: http://arxiv.org/abs/2404.03586v1
• Date: Thu, 4 Apr 2024 16:52:17 GMT
• Title: Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
• Authors: Tyler Chang, Andrew Gillette, Romit Maulik,
• Abstract summary: We present a best-of-both-worlds approach to verifiable scientific machine learning. We show that multiple standard techniques have informative error bounds that can be computed or estimated efficiently. We present a case study of our approach for predicting lift-drag ratios from airfoil images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository.

Related papers

• PETScML: Second-order solvers for training regression problems in Scientific Machine Learning [0.22499166814992438]
  In recent years, we have witnessed the emergence of scientific machine learning as a data-driven tool for the analysis. We introduce a software built on top of the Portable and Extensible Toolkit for Scientific computation to bridge the gap between deep-learning software and conventional machine-learning techniques.
  arXiv  Detail & Related papers  (2024-03-18T18:59:42Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing [16.02193274044797]
  We propose a new interpretable deep learning method for hyperspectral unmixing that accounts for nonlinearity and endmember variability. The model is learned end-to-end using backpropagation, and trained using a self-supervised strategy. Experimental results on synthetic and real datasets illustrate the performance of the proposed method.
  arXiv  Detail & Related papers  (2023-10-03T18:21:37Z)
• Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics [87.05961347040237]
  We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching. Our framework uses machine learning to learn an optimal metric for matching units and estimating outcomes. We show empirically that instances of Matched Machine Learning perform on par with black-box machine learning methods and better than existing matching methods for similar problems.
  arXiv  Detail & Related papers  (2023-04-03T19:32:30Z)
• Interpretable models for extrapolation in scientific machine learning [0.0]
  Complex machine learning algorithms often outperform simple regressions in interpolative settings. We examine the trade-off between model performance and interpretability across a broad range of science and engineering problems.
  arXiv  Detail & Related papers  (2022-12-16T19:33:28Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Distilling Interpretable Models into Human-Readable Code [71.11328360614479]
  Human-readability is an important and desirable standard for machine-learned model interpretability. We propose to train interpretable models using conventional methods, and then distill them into concise, human-readable code. We describe a piecewise-linear curve-fitting algorithm that produces high-quality results efficiently and reliably across a broad range of use cases.
  arXiv  Detail & Related papers  (2021-01-21T01:46:36Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• Learning High Order Feature Interactions with Fine Control Kernels [12.5433010409486]
  We provide a methodology for learning sparse statistical models that use as features all possible multiplicative interactions. We also introduce an algorithmic paradigm, the Fine Control Kernel framework, so named because it is based on Fenchel Duality.
  arXiv  Detail & Related papers  (2020-02-09T06:29:15Z)
• Active Learning in Video Tracking [8.782204980889079]
  We propose an adversarial approach for active learning with structured prediction domains that is tractable for matching. We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos.
  arXiv  Detail & Related papers  (2019-12-29T00:42:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.