Prediction Confidence from Neighbors

• URL: http://arxiv.org/abs/2003.14047v1
• Date: Tue, 31 Mar 2020 09:26:09 GMT
• Title: Prediction Confidence from Neighbors
• Authors: Mark Philip Philipsen and Thomas Baltzer Moeslund
• Abstract summary: The inability of Machine Learning (ML) models to successfully extrapolate correct predictions from out-of-distribution (OoD) samples is a major hindrance to the application of ML in critical applications. We show that feature space distance is a meaningful measure that can provide confidence in predictions. This enables earlier and safer deployment of models in critical applications and is vital for deploying models under ever-changing conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The inability of Machine Learning (ML) models to successfully extrapolate correct predictions from out-of-distribution (OoD) samples is a major hindrance to the application of ML in critical applications. Until the generalization ability of ML methods is improved it is necessary to keep humans in the loop. The need for human supervision can only be reduced if it is possible to determining a level of confidence in predictions, which can be used to either ask for human assistance or to abstain from making predictions. We show that feature space distance is a meaningful measure that can provide confidence in predictions. The distance between unseen samples and nearby training samples proves to be correlated to the prediction error of unseen samples. Depending on the acceptable degree of error, predictions can either be trusted or rejected based on the distance to training samples. %Additionally, a novelty threshold can be used to decide whether a sample is worth adding to the training set. This enables earlier and safer deployment of models in critical applications and is vital for deploying models under ever-changing conditions.

Related papers

• Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis [0.34473740271026115]
  Spotting Machine Learning failures is of paramount importance when ML predictions are used to drive clinical decisions. We propose a simple approach that can be used in the deployment phase of any ML model to suggest whether to trust predictions or not. Our method holds the promise to provide effective support to clinicians by spotting potential ML failures during deployment.
  arXiv  Detail & Related papers  (2024-02-27T14:48:07Z)
• Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests [0.0]
  Conformal Prediction has emerged as a promising method to pair machine learning models with prediction intervals. We propose a method to estimate the uncertainty of each sample by calculating the variance obtained from a Deep Regression Forest.
  arXiv  Detail & Related papers  (2024-02-21T19:09:53Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Conformal prediction for the design problem [72.14982816083297]
  In many real-world deployments of machine learning, we use a prediction algorithm to choose what data to test next. In such settings, there is a distinct type of distribution shift between the training and test data. We introduce a method to quantify predictive uncertainty in such settings.
  arXiv  Detail & Related papers  (2022-02-08T02:59:12Z)
• Learning to Predict Trustworthiness with Steep Slope Loss [69.40817968905495]
  We study the problem of predicting trustworthiness on real-world large-scale datasets. We observe that the trustworthiness predictors trained with prior-art loss functions are prone to view both correct predictions and incorrect predictions to be trustworthy. We propose a novel steep slope loss to separate the features w.r.t. correct predictions from the ones w.r.t. incorrect predictions by two slide-like curves that oppose each other.
  arXiv  Detail & Related papers  (2021-09-30T19:19:09Z)
• Better sampling in explanation methods can prevent dieselgate-like deception [0.0]
  Interpretability of prediction models is necessary to determine their biases and causes of errors. Popular techniques, such as IME, LIME, and SHAP, use perturbation of instance features to explain individual predictions. We show that the improved sampling increases the robustness of the LIME and SHAP, while previously untested method IME is already the most robust of all.
  arXiv  Detail & Related papers  (2021-01-26T13:41:37Z)
• Robust Validation: Confident Predictions Even When Distributions Shift [19.327409270934474]
  We describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it.
  arXiv  Detail & Related papers  (2020-08-10T17:09:16Z)
• Individual Calibration with Randomized Forecasting [116.2086707626651]
  We show that calibration for individual samples is possible in the regression setup if the predictions are randomized. We design a training objective to enforce individual calibration and use it to train randomized regression functions.
  arXiv  Detail & Related papers  (2020-06-18T05:53:10Z)

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.