Download Conformal Prediction for Reliable Machine Learning. Theory, by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk PDF

By Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

The conformal predictions framework is a contemporary improvement in computing device studying that may affiliate a competent degree of self belief with a prediction in any real-world trend acceptance program, together with risk-sensitive functions corresponding to clinical prognosis, face reputation, and fiscal hazard prediction. Conformal Predictions for trustworthy computing device studying: conception, diversifications and Applications captures the elemental thought of the framework, demonstrates the best way to use it on real-world difficulties, and provides a number of diversifications, together with lively studying, switch detection, and anomaly detection. As practitioners and researchers all over the world follow and adapt the framework, this edited quantity brings jointly those our bodies of labor, delivering a springboard for additional learn 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 computing device learning
  • Be capable of practice this framework to real-world difficulties in several desktop studying settings, together with category, regression, and clustering
  • Learn potent methods of adapting the framework to more recent challenge settings, comparable to energetic studying, version choice, or switch detection

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Extra info for Conformal Prediction for Reliable Machine Learning. Theory, Adaptations and Applications

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Tukey’s description, however, is quite different; of course, Tukey defines his tolerance predictor directly, not via a nonconformity measure. The parameters of his predictor are a sequence of measurable functions (φ1 , . . , φl ) on Z and a nonempty set λ ⊂ {1, . . , l, l + 1}. Let (z 1 , . . , zl ) be a sequence of examples (what we call a training set). Tukey splits a subset of Z of Q-measure 1 into a sequence of “statistically 31 32 CHAPTER 2 Beyond the Basic Conformal Prediction Framework equivalent blocks” S1 , .

Z n be a sequence of examples z i = (xi , yi ); the corresponding categories and conformity scores are defined as follows. Partition X = [0, 1]d into (1/h n )d axis-parallel cubes with sides of length 1 log n d+3 hn . n Define the category κi of z i as the cell of the partition containing xi . Let A be a cell of the partition. 4 (cf. 12)) ˆ | A) arbitrarily). Finally, set the and n A := |{i | xi ∈ A}| (if n A = 0, define p(y ˆ i | Ai ), where Ai is the cell of the partition that conformity scores to αi := p(y contains xi .

It would also be interesting to develop object and label conditional versions of Tukey’s predictors. Among the developments of Tukey’s procedure in other directions are Fraser’s [101], who noticed that we can allow φk to depend on the maxima reached by φ1 , . . , φk−1 in Tukey’s procedure described earlier, and Kemperman’s [172], who further noticed that we can allow dependence on the examples were the maxima were reached rather than on the maxima themselves. Wilks’s [379] procedure is a special case of Tukey’s procedure in which Z = R and φ1 = · · · = φl are the identity functions φi (z) = z.

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