Predictive Inference with Weak Supervision

• URL: http://arxiv.org/abs/2201.08315v1
• Date: Thu, 20 Jan 2022 17:26:52 GMT
• Title: Predictive Inference with Weak Supervision
• Authors: Maxime Cauchois, Suyash Gupta, Alnur Ali, John Duchi
• Abstract summary: We bridge the gap between partial supervision and validation by developing a conformal prediction framework. We introduce a new notion of coverage and predictive validity, then develop several application scenarios. We corroborate the hypothesis that the new coverage definition allows for tighter and more informative (but valid) confidence sets.
• Score: 3.1925030748447747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The expense of acquiring labels in large-scale statistical machine learning makes partially and weakly-labeled data attractive, though it is not always apparent how to leverage such data for model fitting or validation. We present a methodology to bridge the gap between partial supervision and validation, developing a conformal prediction framework to provide valid predictive confidence sets -- sets that cover a true label with a prescribed probability, independent of the underlying distribution -- using weakly labeled data. To do so, we introduce a (necessary) new notion of coverage and predictive validity, then develop several application scenarios, providing efficient algorithms for classification and several large-scale structured prediction problems. We corroborate the hypothesis that the new coverage definition allows for tighter and more informative (but valid) confidence sets through several experiments.

Related papers

• Robust Conformal Prediction Using Privileged Information [17.886554223172517]
  We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data. Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption.
  arXiv  Detail & Related papers  (2024-06-08T08:56:47Z)
• Binary Classification with Confidence Difference [100.08818204756093]
  This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
  arXiv  Detail & Related papers  (2023-10-09T11:44:50Z)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  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.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)
• Query-Adaptive Predictive Inference with Partial Labels [0.0]
  We propose a new methodology to construct predictive sets using only partially labeled data on top of black-box predictive models. Our experiments highlight the validity of our predictive set construction as well as the attractiveness of a more flexible user-dependent loss framework.
  arXiv  Detail & Related papers  (2022-06-15T01:48:42Z)
• Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee [4.821312633849745]
  We develop a series of conformal inference methods, including building predictive sets and inferring outliers for complex and high-dimensional data. We evaluate our method, robust flow-based conformal inference, on benchmark datasets.
  arXiv  Detail & Related papers  (2022-05-22T04:17:30Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Distribution-Free, Risk-Controlling Prediction Sets [112.9186453405701]
  We show how to generate set-valued predictions from a black-box predictor that control the expected loss on future test points at a user-specified level. Our approach provides explicit finite-sample guarantees for any dataset by using a holdout set to calibrate the size of the prediction sets.
  arXiv  Detail & Related papers  (2021-01-07T18:59:33Z)
• 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)
• 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)
• Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction [0.8594140167290097]
  We develop conformal prediction methods for constructing valid confidence sets in multiclass and multilabel problems. By leveraging ideas from quantile regression, we build methods that always guarantee correct coverage but additionally provide conditional coverage for both multiclass and multilabel prediction problems.
  arXiv  Detail & Related papers  (2020-04-21T17:45:38Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.