Retrain or not retrain: Conformal test martingales for change-point detection

• URL: http://arxiv.org/abs/2102.10439v1
• Date: Sat, 20 Feb 2021 20:39:05 GMT
• Title: Retrain or not retrain: Conformal test martingales for change-point detection
• Authors: Vladimir Vovk, Ivan Petej, Ilia Nouretdinov, Ernst Ahlberg, Lars Carlsson, and Alex Gammerman
• Abstract summary: We argue for supplementing the process of training a prediction algorithm by setting up a scheme for detecting the moment when the distribution of the data changes. Our proposed schemes are based on exchangeability martingales, i.e., processes that are martingales under any exchangeable distribution for the data.
• Score: 0.34635278365524663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We argue for supplementing the process of training a prediction algorithm by setting up a scheme for detecting the moment when the distribution of the data changes and the algorithm needs to be retrained. Our proposed schemes are based on exchangeability martingales, i.e., processes that are martingales under any exchangeable distribution for the data. Our method, based on conformal prediction, is general and can be applied on top of any modern prediction algorithm. Its validity is guaranteed, and in this paper we make first steps in exploring its efficiency.

Related papers

• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors. We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
  arXiv  Detail & Related papers  (2024-09-27T21:46:42Z)
• Distribution Shift Inversion for Out-of-Distribution Prediction [57.22301285120695]
  We propose a portable Distribution Shift Inversion algorithm for Out-of-Distribution (OoD) prediction. We show that our method provides a general performance gain when plugged into a wide range of commonly used OoD algorithms.
  arXiv  Detail & Related papers  (2023-06-14T08:00:49Z)
• Adapting to Continuous Covariate Shift via Online Density Ratio Estimation [64.8027122329609]
  Dealing with distribution shifts is one of the central challenges for modern machine learning. We propose an online method that can appropriately reuse historical information. Our density ratio estimation method is proven to perform well by enjoying a dynamic regret bound.
  arXiv  Detail & Related papers  (2023-02-06T04:03:33Z)
• Conformal Inference for Online Prediction with Arbitrary Distribution Shifts [1.2277343096128712]
  We consider the problem of forming prediction sets in an online setting where the distribution generating the data is allowed to vary over time. We develop a novel procedure with provably small regret over all local time intervals of a given width. We test our techniques on two real-world datasets aimed at predicting stock market volatility and COVID-19 case counts.
  arXiv  Detail & Related papers  (2022-08-17T16:51:12Z)
• Accuracy Convergent Field Predictors [0.0]
  Methods are described on how to adapt algorithms in order to make them achieve predictive accuracy convergence. Highlighted are variants that make predictions by superposing fields associated to the training data instances.
  arXiv  Detail & Related papers  (2022-05-07T20:02:43Z)
• Non-Clairvoyant Scheduling with Predictions Revisited [77.86290991564829]
  In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a priori unknown processing requirements. We revisit this well-studied problem in a recently popular learning-augmented setting that integrates (untrusted) predictions in algorithm design. We show that these predictions have desired properties, admit a natural error measure as well as algorithms with strong performance guarantees.
  arXiv  Detail & Related papers  (2022-02-21T13:18:11Z)
• Adaptive Conformal Inference Under Distribution Shift [0.0]
  We develop methods for forming prediction sets in an online setting where the data generating distribution is allowed to vary over time in an unknown fashion. Our framework builds on ideas from conformal inference to provide a general wrapper that can be combined with any black box method. We test our method, adaptive conformal inference, on two real world datasets and find that its predictions are robust to visible and significant distribution shifts.
  arXiv  Detail & Related papers  (2021-06-01T01:37:32Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms [67.67377846416106]
  We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We show that value-based methods such as TD($lambda$) and $Q$-Learning have update rules which are contractive in the space of distributions of functions.
  arXiv  Detail & Related papers  (2020-03-27T05:13:29Z)
• Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation [25.97333838935589]
  Uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. Existing algorithms can overestimate certainty, possibly yielding a false sense of confidence in the predictive model.
  arXiv  Detail & Related papers  (2020-02-29T20:31:04Z)
• Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift [8.068725688880772]
  Most predictive models assume that training and test data are generated from a stationary process. We consider the scenario of a gradual concept drift due to the underlying non-stationarity of the data source. We propose a novel, iterative algorithm for unsupervised adaptation of predictive models.
  arXiv  Detail & Related papers  (2020-02-10T21:29:09Z)

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.