Machine-Learning Applications of Algorithmic Randomness

By Vladimir G Vovk, A. Gammerman, Craig John Saunders. 1999

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (2018, positive): “builds on an existing ML classifier to provide a probabilistically valid measure of confidence and credibility for predictions made by the underlying classifier [19], [20], [24].”
Conformalized Quantile Regression (2019, background): “We now describe how conformal prediction [1, 3] constructs prediction intervals that satisfy the finitesample coverage guarantee (1).”
Conditional validity of inductive conformal predictors (2012, methodology): “l validity and various ways to achieve them using inductive conformal predictors and their modications. 1 Introduction This paper continues study of the method of conformal prediction, introduced in Vovk et al. (1999) and Saunders et al. (1999) and further developed in Vovk et al. (2005). An advanta”
Conformal Risk Control (2022, methodology): “Conformal prediction was developed by Vladimir Vovk and collaborators beginning in the late 1990s (Vovk et al., 1999; 2005), and has recently become a popular uncertainty estimation tool in the machine learning community, due to its favorable model-agnostic, distribution-free, finite-sample…”
Distribution-Free, Risk-Controlling Prediction Sets (2021, background): “In parallel, conformal prediction [8, 9] has been recognized as an attractive way of producing predictive sets with finite-sample guarantees.”
Inductive Conformal Prediction: Theory and Application to Neural Networks (2008, background): “, 1999) and (Vovk et al., 1999), where what we call in this chapter “Conformal Prediction” (CP) was proposed.”
Uncertainty Sets for Image Classifiers using Conformal Prediction (2020, unknown): “(1) (Vovk et al., 1999; 2005).”
A comparison of some conformal quantile regression methods (2019, background): “|X and building a fixed-width band around it (Vovk et al., 1999; Vovk et al., 2005; Vovk et al., 2009; Lei et al., 2018).”
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification (2021, methodology): “The framework we now call conformal prediction was hatched by Vladimir Vovk, Alexander Gammerman, Craig Saunders, and Vladimir Vapnik in the years 1996-1999, ﬁrst using e-values [50] and then with p-values [51, 52].”
With Malice Towards None: Assessing Uncertainty via Equalized Coverage (2019, methodology): “Such a procedure can be formulated by reﬁning tools from conformal inference, a general methodology for constructing prediction intervals [18–24].”
Inductive Confidence Machines for Regression (2002, background): “Vovk, Gammerman, and Saunders (1999) introduced the universal confidence values: the best confidence values one can hope to obtain.”