Predictive Inference in Multi-environment Scenarios

• URL: http://arxiv.org/abs/2403.16336v2
• Date: Wed, 13 Nov 2024 14:13:58 GMT
• Title: Predictive Inference in Multi-environment Scenarios
• Authors: John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur,
• Abstract summary: We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments. We extend the jackknife and split-conformal methods to show how to obtain distribution-free coverage in non-traditional, potentially hierarchical data-generating scenarios. Our contributions also include extensions for settings with non-real-valued responses, a theory of consistency for predictive inference in these general problems, and insights on the limits of conditional coverage.
• Score: 18.324321417099394
• License:
• Abstract: We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments. We investigate two types of coverage suitable for these problems, extending the jackknife and split-conformal methods to show how to obtain distribution-free coverage in such non-traditional, potentially hierarchical data-generating scenarios. We demonstrate a novel resizing method to adapt to problem difficulty, which applies both to existing approaches for predictive inference and the methods we develop; this reduces prediction set sizes using limited information from the test environment, a key to the methods' practical performance, which we evaluate through neurochemical sensing and species classification datasets. Our contributions also include extensions for settings with non-real-valued responses, a theory of consistency for predictive inference in these general problems, and insights on the limits of conditional coverage.

Related papers

• Prediction-Powered E-Values [0.66567375919026]
  We apply ideas of prediction-powered inference to e-values. We show that every inference procedure that can be framed in terms of e-values has a prediction-powered counterpart. Our approach is modular and easily integrable into existing algorithms.
  arXiv  Detail & Related papers  (2025-02-06T18:36:01Z)
• Can We Validate Counterfactual Estimations in the Presence of General Network Interference? [6.092214762701847]
  We introduce a new framework enabling cross-validation for counterfactual estimation. At its core is our distribution-preserving network bootstrap method. We extend recent causal message-passing developments by incorporating heterogeneous unit-level characteristics.
  arXiv  Detail & Related papers  (2025-02-03T06:51:04Z)
• Noise-Adaptive Conformal Classification with Marginal Coverage [53.74125453366155]
  We introduce an adaptive conformal inference method capable of efficiently handling deviations from exchangeability caused by random label noise. We validate our method through extensive numerical experiments demonstrating its effectiveness on synthetic and real data sets.
  arXiv  Detail & Related papers  (2025-01-29T23:55:23Z)
• C$^{2}$INet: Realizing Incremental Trajectory Prediction with Prior-Aware Continual Causal Intervention [10.189508227447401]
  Trajectory prediction for multi-agents in complex scenarios is crucial for applications like autonomous driving. Existing methods often overlook environmental biases, which leads to poor generalization. We propose the Continual Causal Intervention (C$2$INet) method for generalizable multi-agent trajectory prediction.
  arXiv  Detail & Related papers  (2024-11-19T08:01:20Z)
• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift [9.387706860375461]
  A distribution shift occurs when the underlying data-generating process changes, leading to a deviation in the model's performance. The prediction interval serves as a crucial tool for characterizing uncertainties induced by their underlying distribution. We propose methodologies for aggregating prediction intervals to obtain one with minimal width and adequate coverage on the target domain.
  arXiv  Detail & Related papers  (2024-05-16T17:55:42Z)
• Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale [53.152460508207184]
  Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data. This paper proposes a novel approach that considers multiple prediction hypotheses for each sample and investigates the rationale behind each hypothesis. To achieve the optimal performance, we propose a three-step adaptation process: model pre-adaptation, hypothesis consolidation, and semi-supervised learning.
  arXiv  Detail & Related papers  (2024-02-02T05:53:22Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Learning Invariant Representations under General Interventions on the Response [2.725698729450241]
  We focus on linear structural causal models (SCMs) and introduce invariant matching property (IMP) We analyze the generalization errors of our method under both the discrete and continuous environment settings.
  arXiv  Detail & Related papers  (2022-08-22T03:09:17Z)
• A One-step Approach to Covariate Shift Adaptation [82.01909503235385]
  A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution. We propose a novel one-step approach that jointly learns the predictive model and the associated weights in one optimization.
  arXiv  Detail & Related papers  (2020-07-08T11:35:47Z)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

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.