Algebraic Ground Truth Inference: Non-Parametric Estimation of Sample Errors by AI Algorithms

• URL: http://arxiv.org/abs/2006.08312v1
• Date: Mon, 15 Jun 2020 12:04:47 GMT
• Title: Algebraic Ground Truth Inference: Non-Parametric Estimation of Sample Errors by AI Algorithms
• Authors: Andr\'es Corrada-Emmanuel and Edward Pantridge and Edward Zahrebelski and Aditya Chaganti and Simeon Simeonov
• Abstract summary: Non-parametric estimators of performance are an attractive solution in autonomous settings. We show that the accuracy estimators in the experiments where we have ground truth are better than one part in a hundred. The practical utility of the method is illustrated on a real-world dataset from an online advertising campaign and a sample of common classification benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Binary classification is widely used in ML production systems. Monitoring classifiers in a constrained event space is well known. However, real world production systems often lack the ground truth these methods require. Privacy concerns may also require that the ground truth needed to evaluate the classifiers cannot be made available. In these autonomous settings, non-parametric estimators of performance are an attractive solution. They do not require theoretical models about how the classifiers made errors in any given sample. They just estimate how many errors there are in a sample of an industrial or robotic datastream. We construct one such non-parametric estimator of the sample errors for an ensemble of weak binary classifiers. Our approach uses algebraic geometry to reformulate the self-assessment problem for ensembles of binary classifiers as an exact polynomial system. The polynomial formulation can then be used to prove - as an algebraic geometry algorithm - that no general solution to the self-assessment problem is possible. However, specific solutions are possible in settings where the engineering context puts the classifiers close to independent errors. The practical utility of the method is illustrated on a real-world dataset from an online advertising campaign and a sample of common classification benchmarks. The accuracy estimators in the experiments where we have ground truth are better than one part in a hundred. The online advertising campaign data, where we do not have ground truth data, is verified by an internal consistency approach whose validity we conjecture as an algebraic geometry theorem. We call this approach - algebraic ground truth inference.

Related papers

• Bisimulation Learning [55.859538562698496]
  We compute finite bisimulations of state transition systems with large, possibly infinite state space. Our technique yields faster verification results than alternative state-of-the-art tools in practice.
  arXiv  Detail & Related papers  (2024-05-24T17:11:27Z)
• Out-Of-Domain Unlabeled Data Improves Generalization [0.7589678255312519]
  We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems. We show that unlabeled samples can be harnessed to narrow the generalization gap. We validate our claims through experiments conducted on a variety of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-09-29T02:00:03Z)
• Who Should Predict? Exact Algorithms For Learning to Defer to Humans [40.22768241509553]
  We show that prior approaches can fail to find a human-AI system with low misclassification error. We give a mixed-integer-linear-programming (MILP) formulation that can optimally solve the problem in the linear setting. We provide a novel surrogate loss function that is realizable-consistent and performs well empirically.
  arXiv  Detail & Related papers  (2023-01-15T21:57:36Z)
• Class-wise and reduced calibration methods [0.0]
  We show how a reduced calibration method transforms the original problem into a simpler one. Second, we propose class-wise calibration methods, based on building on a phenomenon called neural collapse. Applying the two methods together results in class-wise reduced calibration algorithms, which are powerful tools for reducing the prediction and per-class calibration errors.
  arXiv  Detail & Related papers  (2022-10-07T17:13:17Z)
• Predicting Out-of-Domain Generalization with Neighborhood Invariance [59.05399533508682]
  We propose a measure of a classifier's output invariance in a local transformation neighborhood. Our measure is simple to calculate, does not depend on the test point's true label, and can be applied even in out-of-domain (OOD) settings. In experiments on benchmarks in image classification, sentiment analysis, and natural language inference, we demonstrate a strong and robust correlation between our measure and actual OOD generalization.
  arXiv  Detail & Related papers  (2022-07-05T14:55:16Z)
• Test Set Sizing Via Random Matrix Theory [91.3755431537592]
  This paper uses techniques from Random Matrix Theory to find the ideal training-testing data split for a simple linear regression. It defines "ideal" as satisfying the integrity metric, i.e. the empirical model error is the actual measurement noise. This paper is the first to solve for the training and test size for any model in a way that is truly optimal.
  arXiv  Detail & Related papers  (2021-12-11T13:18:33Z)
• High-dimensional separability for one- and few-shot learning [58.8599521537]
  This work is driven by a practical question, corrections of Artificial Intelligence (AI) errors. Special external devices, correctors, are developed. They should provide quick and non-iterative system fix without modification of a legacy AI system. New multi-correctors of AI systems are presented and illustrated with examples of predicting errors and learning new classes of objects by a deep convolutional neural network.
  arXiv  Detail & Related papers  (2021-06-28T14:58:14Z)
• Evaluating State-of-the-Art Classification Models Against Bayes Optimality [106.50867011164584]
  We show that we can compute the exact Bayes error of generative models learned using normalizing flows. We use our approach to conduct a thorough investigation of state-of-the-art classification models.
  arXiv  Detail & Related papers  (2021-06-07T06:21:20Z)
• Independence Tests Without Ground Truth for Noisy Learners [0.0]
  We discuss the exact solution for independent binary classifiers. Its practical applicability is hampered by its sole remaining assumption. A similar conjecture for the ground truth invariant system for scalar regressors is solvable.
  arXiv  Detail & Related papers  (2020-10-28T13:03:26Z)
• 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)

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.