Optimized conformal classification using gradient descent approximation

• URL: http://arxiv.org/abs/2105.11255v1
• Date: Mon, 24 May 2021 13:14:41 GMT
• Title: Optimized conformal classification using gradient descent approximation
• Authors: Anthony Bellotti
• Abstract summary: Conformal predictors allow predictions to be made with a user-defined confidence level. We consider an approach to train the conformal predictor directly with maximum predictive efficiency. We test the method on several real world data sets and find that the method is promising.
• Score: 0.2538209532048866
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conformal predictors are an important class of algorithms that allow predictions to be made with a user-defined confidence level. They are able to do this by outputting prediction sets, rather than simple point predictions. The conformal predictor is valid in the sense that the accuracy of its predictions is guaranteed to meet the confidence level, only assuming exchangeability in the data. Since accuracy is guaranteed, the performance of a conformal predictor is measured through the efficiency of the prediction sets. Typically, a conformal predictor is built on an underlying machine learning algorithm and hence its predictive power is inherited from this algorithm. However, since the underlying machine learning algorithm is not trained with the objective of minimizing predictive efficiency it means that the resulting conformal predictor may be sub-optimal and not aligned sufficiently to this objective. Hence, in this study we consider an approach to train the conformal predictor directly with maximum predictive efficiency as the optimization objective, and we focus specifically on the inductive conformal predictor for classification. To do this, the conformal predictor is approximated by a differentiable objective function and gradient descent used to optimize it. The resulting parameter estimates are then passed to a proper inductive conformal predictor to give valid prediction sets. We test the method on several real world data sets and find that the method is promising and in most cases gives improved predictive efficiency against a baseline conformal predictor.

Related papers

• Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores [52.92618442300405]
  It is impossible to achieve exact, distribution-free conditional coverage in finite samples. We propose an alternative conformal prediction algorithm that targets coverage where it matters most.
  arXiv  Detail & Related papers  (2025-01-17T12:01:56Z)
• Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning [53.42244686183879]
  Conformal prediction provides model-agnostic and distribution-free uncertainty quantification. Yet, conformal prediction is not reliable under poisoning attacks where adversaries manipulate both training and calibration data. We propose reliable prediction sets (RPS): the first efficient method for constructing conformal prediction sets with provable reliability guarantees under poisoning.
  arXiv  Detail & Related papers  (2024-10-13T15:37:11Z)
• Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors [0.0]
  Conformal prediction requires exchangeable data to ensure valid prediction sets at a user-specified significance level. Adaptive conformal inference (ACI) was introduced to address this limitation. We show that ACI does not require the use of conformal predictors; instead, it can be implemented with the more general confidence predictors.
  arXiv  Detail & Related papers  (2024-09-23T21:02:33Z)
• Towards Human-AI Complementarity with Prediction Sets [14.071862670474832]
  Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. We show that the prediction sets constructed using conformal prediction are, in general, suboptimal in terms of average accuracy. We introduce a greedy algorithm that, for a large class of expert models and non-optimal scores, is guaranteed to find prediction sets that provably offer equal or greater performance.
  arXiv  Detail & Related papers  (2024-05-27T18:00:00Z)
• Does confidence calibration improve conformal prediction? [10.340903334800787]
  We show that current confidence calibration methods lead to larger prediction sets in adaptive conformal prediction. By investigating the role of temperature value, we observe that high-confidence predictions can enhance the efficiency of adaptive conformal prediction. We propose Conformal Temperature Scaling (ConfTS), a variant of temperature scaling with a novel loss function designed to enhance the efficiency of prediction sets.
  arXiv  Detail & Related papers  (2024-02-06T19:27:48Z)
• Predictive Inference with Feature Conformal Prediction [80.77443423828315]
  We propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods.
  arXiv  Detail & Related papers  (2022-10-01T02:57:37Z)
• Efficient and Differentiable Conformal Prediction with General Function Classes [96.74055810115456]
  We propose a generalization of conformal prediction to multiple learnable parameters. We show that it achieves approximate valid population coverage and near-optimal efficiency within class. Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly.
  arXiv  Detail & Related papers  (2022-02-22T18:37:23Z)
• CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction [71.31516599226606]
  We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories. Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables.
  arXiv  Detail & Related papers  (2021-09-07T09:38:24Z)
• Private Prediction Sets [72.75711776601973]
  Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
  arXiv  Detail & Related papers  (2021-02-11T18:59:11Z)

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.