Derandomized Novelty Detection with FDR Control via Conformal E-values

• URL: http://arxiv.org/abs/2302.07294v3
• Date: Mon, 23 Oct 2023 19:00:39 GMT
• Title: Derandomized Novelty Detection with FDR Control via Conformal E-values
• Authors: Meshi Bashari, Amir Epstein, Yaniv Romano, Matteo Sesia
• Abstract summary: We propose to make conformal inferences more stable by leveraging suitable conformal e-values instead of p-values. We show that the proposed method can reduce randomness without much loss of power compared to standard conformal inference.
• Score: 20.864605211132663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conformal inference provides a general distribution-free method to rigorously calibrate the output of any machine learning algorithm for novelty detection. While this approach has many strengths, it has the limitation of being randomized, in the sense that it may lead to different results when analyzing twice the same data, and this can hinder the interpretation of any findings. We propose to make conformal inferences more stable by leveraging suitable conformal e-values instead of p-values to quantify statistical significance. This solution allows the evidence gathered from multiple analyses of the same data to be aggregated effectively while provably controlling the false discovery rate. Further, we show that the proposed method can reduce randomness without much loss of power compared to standard conformal inference, partly thanks to an innovative way of weighting conformal e-values based on additional side information carefully extracted from the same data. Simulations with synthetic and real data confirm this solution can be effective at eliminating random noise in the inferences obtained with state-of-the-art alternative techniques, sometimes also leading to higher power.

Related papers

• Assumption-Lean Post-Integrated Inference with Negative Control Outcomes [0.0]
  We introduce a robust post-integrated inference (PII) method that adjusts for latent heterogeneity using negative control outcomes. Our method extends to projected direct effect estimands, accounting for hidden mediators, confounders, and moderators. The proposed doubly robust estimators are consistent and efficient under minimal assumptions and potential misspecification.
  arXiv  Detail & Related papers  (2024-10-07T12:52:38Z)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Efficient Conformal Prediction under Data Heterogeneity [79.35418041861327]
  Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification. Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond the simplest examples. This work introduces a new efficient approach to CP that produces provably valid confidence sets for fairly general non-exchangeable data distributions.
  arXiv  Detail & Related papers  (2023-12-25T20:02:51Z)
• Towards stable real-world equation discovery with assessing differentiating quality influence [52.2980614912553]
  We propose alternatives to the commonly used finite differences-based method. We evaluate these methods in terms of applicability to problems, similar to the real ones, and their ability to ensure the convergence of equation discovery algorithms.
  arXiv  Detail & Related papers  (2023-11-09T23:32:06Z)
• Non-Exchangeable Conformal Risk Control [12.381447108228635]
  Split conformal prediction has recently sparked great interest due to its ability to provide formally guaranteed uncertainty sets or intervals. We propose non-exchangeable conformal risk control, which allows controlling the expected value of any monotone loss function when the data is not exchangeable. Our framework is flexible, makes very few assumptions, and allows weighting the data based on its relevance for a given test example.
  arXiv  Detail & Related papers  (2023-10-02T15:00:19Z)
• Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions [59.605246463200736]
  We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions. First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states. We use state-of-the-art verification techniques to provide guarantees on the interval Markov decision process and compute a controller for which these guarantees carry over to the original control system.
  arXiv  Detail & Related papers  (2023-01-04T10:40:30Z)
• Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries [35.67445122503686]
  This paper develops conformal inference methods to construct a confidence interval for the frequency of a queried object in a very large discrete data set. We show our methods have improved empirical performance compared to existing frequentist and Bayesian alternatives in simulations.
  arXiv  Detail & Related papers  (2022-11-09T00:05:29Z)
• Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers [1.6371837018687636]
  This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set. The described methods can re-weight standard conformal p-values based on dependent side information from known out-of-distribution data. The solution can be implemented either through sample splitting or via a novel transductive cross-validation+ scheme.
  arXiv  Detail & Related papers  (2022-08-23T17:52:20Z)
• Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise [59.47042225257565]
  We present a novel planning method that does not rely on any explicit representation of the noise distributions. First, we abstract the continuous system into a discrete-state model that captures noise by probabilistic transitions between states. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP)
  arXiv  Detail & Related papers  (2021-10-25T06:18:55Z)
• Testing for Outliers with Conformal p-values [14.158078752410182]
  The goal is to test whether new independent samples belong to the same distribution as a reference data set or are outliers. We propose a solution based on conformal inference, a broadly applicable framework which yields p-values that are marginally valid but mutually dependent for different test points. We prove these p-values are positively dependent and enable exact false discovery rate control, although in a relatively weak marginal sense.
  arXiv  Detail & Related papers  (2021-04-16T17:59:21Z)
• Balance-Subsampled Stable Prediction [55.13512328954456]
  We propose a novel balance-subsampled stable prediction (BSSP) algorithm based on the theory of fractional factorial design. A design-theoretic analysis shows that the proposed method can reduce the confounding effects among predictors induced by the distribution shift. Numerical experiments on both synthetic and real-world data sets demonstrate that our BSSP algorithm significantly outperforms the baseline methods for stable prediction across unknown test data.
  arXiv  Detail & Related papers  (2020-06-08T07:01:38Z)

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.