Download e-book for iPad: Conformal Prediction for Reliable Machine Learning. Theory, by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

By Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

The conformal predictions framework is a up to date improvement in desktop studying that may affiliate a competent degree of self assurance with a prediction in any real-world trend attractiveness software, together with risk-sensitive functions resembling clinical prognosis, face acceptance, and monetary chance prediction. Conformal Predictions for trustworthy computing device studying: concept, diversifications and Applications captures the elemental conception of the framework, demonstrates tips on how to use it on real-world difficulties, and provides numerous variations, together with lively studying, switch detection, and anomaly detection. As practitioners and researchers all over the world observe and adapt the framework, this edited quantity brings jointly those our bodies of labor, delivering a springboard for additional study in addition to a guide for software in real-world problems.

  • Understand the theoretical foundations of this significant framework which could offer a competent degree of self belief with predictions in computer learning
  • Be capable of observe this framework to real-world difficulties in several laptop studying settings, together with class, regression, and clustering
  • Learn potent methods of adapting the framework to more moderen challenge settings, reminiscent of energetic studying, version choice, or swap detection

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Additional resources for Conformal Prediction for Reliable Machine Learning. Theory, Adaptations and Applications

Example text

For concreteness, let us set C or = {(x, y) | qx (y) ≥ tx }. The following is a special case of Lei and Wasserman’s result. 1 ([201], Theorem 9). Suppose Assumptions 1–7 hold. There exists a conditional conformal predictor (independent of ) such that for any λ > 0 there exists B such that, as l → ∞, P sup (z 1 , . . , zl , x) x∈X C xor ≥B log l l 1 d+3 = O l −λ . 24) is optimal (see [201], Theorem 12). 24). It is determined by the following taxonomy and conditional conformity measure. Let z 1 , .

1 Negative Result We start from a negative result (a version of Lemma 1 in [201]) which says that the requirement of precise object conditional validity cannot be satisfied in a nontrivial way for rich object spaces (such as X = R). If Q is a probability distribution on Z, we let Q X stand for its marginal distribution on X: Q X (A) := Q(A × Y). We will consider randomized set predictors that depend, additionally, on a random input ω ∈ whose distribution (characterizing the generator of random numbers used by the predictor) will be denoted R.

N ); these two sequences are the parameters of the procedure. Suppose we are given a sequence of examples (z 1 , . . 3)). Assign nonconformity score β1 to all z i at which max φ1 (z i ) is attained and discard those z i . Then assign nonconformity score β2 to all z i at which max φ2 (z i ) is attained and discard those z i . Continue doing this until all z i are assigned nonconformity scores and discarded (it is clear that this will happen at step n at the latest). Notice that the last function φn does not play any useful role.

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