Manifold for Machine Learning Assurance

• URL: http://arxiv.org/abs/2002.03147v1
• Date: Sat, 8 Feb 2020 11:39:01 GMT
• Title: Manifold for Machine Learning Assurance
• Authors: Taejoon Byun, Sanjai Rayadurgam
• Abstract summary: We propose an analogous approach for machine-learning (ML) systems using an ML technique that extracts from the high-dimensional training data implicitly describing the required system. It is then harnessed for a range of quality assurance tasks such as test adequacy measurement, test input generation, and runtime monitoring of the target ML system. Preliminary experiments establish that the proposed manifold-based approach, for test adequacy drives diversity in test data, for test generation yields fault-revealing yet realistic test cases, and for runtime monitoring provides an independent means to assess trustability of the target system's output.
• Score: 9.594432031144716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system provide a sound basis for judging its implementation. We posit an analogous approach for ML systems using an ML technique that extracts from the high-dimensional training data implicitly describing the required system, a low-dimensional underlying structure--a manifold. It is then harnessed for a range of quality assurance tasks such as test adequacy measurement, test input generation, and runtime monitoring of the target ML system. The approach is built on variational autoencoder, an unsupervised method for learning a pair of mutually near-inverse functions between a given high-dimensional dataset and a low-dimensional representation. Preliminary experiments establish that the proposed manifold-based approach, for test adequacy drives diversity in test data, for test generation yields fault-revealing yet realistic test cases, and for runtime monitoring provides an independent means to assess trustability of the target system's output.

Related papers

• Discovering Decision Manifolds to Assure Trusted Autonomous Systems [0.0]
  We propose an optimization-based search technique for capturing the range of correct and incorrect responses a system could exhibit. This manifold provides a more detailed understanding of system reliability than traditional testing or Monte Carlo simulations. In this proof-of-concept, we apply our method to a software-in-the-loop evaluation of an autonomous vehicle.
  arXiv  Detail & Related papers  (2024-02-12T16:55:58Z)
• Distributionally Robust Statistical Verification with Imprecise Neural Networks [4.094049541486327]
  A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. This paper proposes a novel approach based on a combination of active learning, uncertainty quantification, and neural network verification.
  arXiv  Detail & Related papers  (2023-08-28T18:06:24Z)
• Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Methods Integrated in Instrumentation & Control Systems [1.8978726202765634]
  Trustworthiness of datadriven neural network-based machine learning algorithms is not adequately assessed. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption. We demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset.
  arXiv  Detail & Related papers  (2023-08-08T18:25:42Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States [4.4820711784498]
  This paper proposes a novel method for the computation of an optimal input trajectory for unknown nonlinear systems with latent states. The effectiveness of the proposed method is demonstrated in a numerical simulation.
  arXiv  Detail & Related papers  (2023-03-31T11:06:09Z)
• The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study [15.016047591601094]
  We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. ML generates input for system, GUI, unit, performance, and testing or improves the performance of existing generation methods.
  arXiv  Detail & Related papers  (2022-06-21T09:26:25Z)
• Supervised DKRC with Images for Offline System Identification [77.34726150561087]
  Modern dynamical systems are becoming increasingly non-linear and complex. There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. Our approach learns these basis functions using a supervised learning approach.
  arXiv  Detail & Related papers  (2021-09-06T04:39:06Z)
• An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
  We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
  arXiv  Detail & Related papers  (2021-08-09T17:39:09Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Active Learning for Nonlinear System Identification with Guarantees [102.43355665393067]
  We study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.
  arXiv  Detail & Related papers  (2020-06-18T04:54:11Z)

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.