Related papers: In Defense of Defensive Forecasting

In Defense of Defensive Forecasting

URL: http://arxiv.org/abs/2506.11848v1
Date: Fri, 13 Jun 2025 14:57:19 GMT
Title: In Defense of Defensive Forecasting
Authors: Juan Carlos Perdomo, Benjamin Recht,
Abstract summary: This tutorial provides a survey of algorithms for Defensive Forecasting, where predictions are derived not by prognostication but by correcting past mistakes.<n>We derive simple, near-optimal algorithms for online learning, calibration, prediction with expert advice, and online conformal prediction.
Score: 13.174466095224403
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This tutorial provides a survey of algorithms for Defensive Forecasting, where predictions are derived not by prognostication but by correcting past mistakes. Pioneered by Vovk, Defensive Forecasting frames the goal of prediction as a sequential game, and derives predictions to minimize metrics no matter what outcomes occur. We present an elementary introduction to this general theory and derive simple, near-optimal algorithms for online learning, calibration, prediction with expert advice, and online conformal prediction.

Related papers

Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method [11.794628063040108]
Multi-Span Question Answering (MSQA) requires models to extract one or multiple answer spans from a given context to answer a question. We propose Answering-Classifying-Correcting (ACC) framework, which employs a post-processing strategy to handle incorrect predictions.
arXiv Detail & Related papers (2024-10-22T08:04:32Z)
Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors.<n>We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
arXiv Detail & Related papers (2024-09-27T21:46:42Z)
Learning-Augmented Algorithms with Explicit Predictors [67.02156211760415]
Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. Prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a black box. In this work, we unpack the predictor and integrate the learning problem it gives rise for within the algorithmic challenge.
arXiv Detail & Related papers (2024-03-12T08:40:21Z)
Predictive Inference with Feature Conformal Prediction [80.77443423828315]
We propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods.
arXiv Detail & Related papers (2022-10-01T02:57:37Z)
Efficient and Differentiable Conformal Prediction with General Function Classes [96.74055810115456]
We propose a generalization of conformal prediction to multiple learnable parameters. We show that it achieves approximate valid population coverage and near-optimal efficiency within class. Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly.
arXiv Detail & Related papers (2022-02-22T18:37:23Z)
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)
Learning Predictions for Algorithms with Predictions [49.341241064279714]
We introduce a general design approach for algorithms that learn predictors. We apply techniques from online learning to learn against adversarial instances, tune robustness-consistency trade-offs, and obtain new statistical guarantees. We demonstrate the effectiveness of our approach at deriving learning algorithms by analyzing methods for bipartite matching, page migration, ski-rental, and job scheduling.
arXiv Detail & Related papers (2022-02-18T17:25:43Z)
Parsimonious Learning-Augmented Caching [29.975391787684966]
We introduce and study the setting in which the learning-augmented algorithm can utilize the predictions parsimoniously. We show that one can achieve quantitatively similar results but only using a sublinear number of predictions.
arXiv Detail & Related papers (2022-02-09T03:40:11Z)
Optimized conformal classification using gradient descent approximation [0.2538209532048866]
Conformal predictors allow predictions to be made with a user-defined confidence level. We consider an approach to train the conformal predictor directly with maximum predictive efficiency. We test the method on several real world data sets and find that the method is promising.
arXiv Detail & Related papers (2021-05-24T13:14:41Z)
Short-term prediction of Time Series based on bounding techniques [0.0]
This paper is reconsidered the prediction problem in time series framework by using a new non-parametric approach. The innovation is to consider both deterministic and deterministic-stochastic assumptions in order to obtain the upper bound of the prediction error. A benchmark is included to illustrate that the proposed predictor can obtain suitable results in a prediction scheme, and can be an interesting alternative method to the classical non-parametric methods.
arXiv Detail & Related papers (2021-01-26T11:27:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.