Single Trajectory Conformal Prediction

• URL: http://arxiv.org/abs/2406.01570v1
• Date: Mon, 3 Jun 2024 17:51:33 GMT
• Title: Single Trajectory Conformal Prediction
• Authors: Brian Lee, Nikolai Matni,
• Abstract summary: We study the performance of risk-controlling prediction sets (RCPS) We use the blocking technique to show that RCPS attains performance guarantees similar to those enjoyed in the iid setting. We conclude by discussing how these tools could be used toward a unified analysis of online and offline conformal prediction algorithms.
• Score: 6.216939610302176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the performance of risk-controlling prediction sets (RCPS), an empirical risk minimization-based formulation of conformal prediction, with a single trajectory of temporally correlated data from an unknown stochastic dynamical system. First, we use the blocking technique to show that RCPS attains performance guarantees similar to those enjoyed in the iid setting whenever data is generated by asymptotically stationary and contractive dynamics. Next, we use the decoupling technique to characterize the graceful degradation in RCPS guarantees when the data generating process deviates from stationarity and contractivity. We conclude by discussing how these tools could be used toward a unified analysis of online and offline conformal prediction algorithms, which are currently treated with very different tools.

Related papers

• Relational Conformal Prediction for Correlated Time Series [56.59852921638328]
  We propose a novel distribution-free approach based on conformal prediction framework and quantile regression. We fill this void by introducing a novel conformal prediction method based on graph deep learning operators. Our approach provides accurate coverage and archives state-of-the-art uncertainty quantification in relevant benchmarks.
  arXiv  Detail & Related papers  (2025-02-13T16:12:17Z)
• Error-quantified Conformal Inference for Time Series [40.438171912992864]
  Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. We propose itError-quantified Conformal Inference (ECI) by smoothing the quantile loss function. ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.
  arXiv  Detail & Related papers  (2025-02-02T15:02:36Z)
• Ranking and Combining Latent Structured Predictive Scores without Labeled Data [2.5064967708371553]
  This paper introduces a novel structured unsupervised ensemble learning model (SUEL) It exploits the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery.
  arXiv  Detail & Related papers  (2024-08-14T20:14:42Z)
• Statistical Reachability Analysis of Stochastic Cyber-Physical Systems under Distribution Shift [0.061446808540639365]
  Reachability analysis is a popular method to give safety guarantees for cyber-physical systems. In this paper, we investigate the problem of performing reachability analysis for an SCPS that does not have a symbolic description of the dynamics. We propose a statistical reachability analysis technique that guarantees that any reachable state during deployment lies in this set with probability not smaller than this threshold.
  arXiv  Detail & Related papers  (2024-07-16T11:18:41Z)
• The Significance of Latent Data Divergence in Predicting System Degradation [1.2058600649065616]
  Condition-Based Maintenance is pivotal in enabling the early detection of potential failures in engineering systems. We introduce a novel methodology grounded in the analysis of statistical similarity within latent data from system components. We infer the similarity between systems by evaluating the divergence of these priors, offering a nuanced understanding of individual system behaviors.
  arXiv  Detail & Related papers  (2024-06-13T11:41:20Z)
• Prediction of rare events in the operation of household equipment using co-evolving time series [1.1249583407496218]
  Our approach involves a weighted autologistic regression model, where we leverage the temporal behavior of the data to enhance predictive capabilities. Evaluation on synthetic and real-world datasets confirms that our approach outperform state-of-the-art of predicting home equipment failure methods.
  arXiv  Detail & Related papers  (2023-12-15T00:21:00Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Self-Certifying Classification by Linearized Deep Assignment [65.0100925582087]
  We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
  arXiv  Detail & Related papers  (2022-01-26T19:59:14Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation [92.96204497841032]
  Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. We propose a model-agnostic data augmentation method that allows us to exploit the prior knowledge of the conditional independence (CI) relations. We experimentally show that the proposed method is effective in improving the prediction accuracy, especially in the small-data regime.
  arXiv  Detail & Related papers  (2021-02-27T06:13:59Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

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.