Improving Adaptive Conformal Prediction Using Self-Supervised Learning

• URL: http://arxiv.org/abs/2302.12238v1
• Date: Thu, 23 Feb 2023 18:57:14 GMT
• Title: Improving Adaptive Conformal Prediction Using Self-Supervised Learning
• Authors: Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar
• Abstract summary: We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
• Score: 72.2614468437919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction is a powerful distribution-free tool for uncertainty quantification, establishing valid prediction intervals with finite-sample guarantees. To produce valid intervals which are also adaptive to the difficulty of each instance, a common approach is to compute normalized nonconformity scores on a separate calibration set. Self-supervised learning has been effectively utilized in many domains to learn general representations for downstream predictors. However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored. In this work, we investigate how self-supervised pretext tasks can improve the quality of the conformal regressors, specifically by improving the adaptability of conformal intervals. We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.

Related papers

• Selective Learning: Towards Robust Calibration with Dynamic Regularization [79.92633587914659]
  Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. We introduce Dynamic Regularization (DReg) which aims to learn what should be learned during training thereby circumventing the confidence adjusting trade-off.
  arXiv  Detail & Related papers  (2024-02-13T11:25:20Z)
• Self-Calibrating Conformal Prediction [16.606421967131524]
  We introduce Self-Calibrating Conformal Prediction to deliver calibrated point predictions alongside prediction intervals with finite-sample validity conditional on these predictions. We show that our method improves calibrated interval efficiency through model calibration and offers a practical alternative to feature-conditional validity.
  arXiv  Detail & Related papers  (2024-02-11T21:12:21Z)
• Absolute convergence and error thresholds in non-active adaptive sampling [0.27624021966289597]
  Non-active adaptive sampling is a way of building machine learning models from a training data base. Proposal for calculating absolute convergence and error thresholds is described. Tests meet our expectations and illustrate the proposal in the domain of natural language processing.
  arXiv  Detail & Related papers  (2024-02-04T15:10:34Z)
• Adaptive scheduling for adaptive sampling in POS taggers construction [0.27624021966289597]
  We introduce an adaptive scheduling for adaptive sampling as a novel way of machine learning in the construction of part-of-speech taggers. We analyze the shape of the learning curve geometrically in conjunction with a functional model to increase or decrease it at any time. We also improve the robustness of sampling by paying greater attention to those regions of the training data base subject to a temporary inflation in performance.
  arXiv  Detail & Related papers  (2024-02-04T15:02:17Z)
• Calibration-Aware Bayesian Learning [37.82259435084825]
  This paper proposes an integrated framework, referred to as calibration-aware Bayesian neural networks (CA-BNNs) It applies both data-dependent or data-independent regularizers while optimizing over a variational distribution as in Bayesian learning. Numerical results validate the advantages of the proposed approach in terms of expected calibration error (ECE) and reliability diagrams.
  arXiv  Detail & Related papers  (2023-05-12T14:19:15Z)
• 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)
• 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)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• Learning Prediction Intervals for Model Performance [1.433758865948252]
  We propose a method to compute prediction intervals for model performance. We evaluate our approach across a wide range of drift conditions and show substantial improvement over competitive baselines.
  arXiv  Detail & Related papers  (2020-12-15T21:32:03Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

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.