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 contemporary improvement in laptop studying which could affiliate a competent degree of self belief with a prediction in any real-world development acceptance program, together with risk-sensitive purposes similar to scientific analysis, face acceptance, and fiscal hazard prediction. Conformal Predictions for trustworthy desktop studying: thought, variations and Applications captures the elemental conception of the framework, demonstrates how you can use it on real-world difficulties, and offers numerous variations, together with energetic studying, switch detection, and anomaly detection. As practitioners and researchers worldwide observe and adapt the framework, this edited quantity brings jointly those our bodies of labor, offering a springboard for additional learn in addition to a instruction manual for program in real-world problems.

  • Understand the theoretical foundations of this significant framework that may offer a competent degree of self assurance with predictions in laptop learning
  • Be capable of practice this framework to real-world difficulties in numerous desktop studying settings, together with type, regression, and clustering
  • Learn powerful methods of adapting the framework to more recent challenge settings, comparable to lively studying, version choice, or swap detection

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

Example text

Let n ∈ N. We can generalize n-taxonomies giving rise to object conditional conformal predictors as follows: a label independent n-taxonomy is a function K that assigns to every sequence (x1 , . . , xn ) ∈ Xn of objects a sequence (κ1 , . . , κn ) ∈ Nn of natural numbers and that is equivariant with respect to permutations: for any permutation π of {1, . . , n}, (κ1 , . . , κn ) = K (x1 , . . , xn ) =⇒ (κπ(1) , . . , κπ(n) ) = K (xπ(1) , . . , xπ(n) ). Intuitively, K clusters x1 , . . , xn , and κi is the cluster assigned to xi .

Zl ) covers all of Z. However, some of the Qs are very unlikely once we know the training set, and the following two-parameter definition captures this intuition. A set predictor is ( , δ)-valid if, for any probability distribution Q on Z, / (z 1 , . . , zl )} ≤ } ≥ 1 − δ. Q l {(z 1 , . . 9) In words, ( , δ)-validity means that with probability at least 1 − δ the probability of the prediction set will be at least 1− . 2 for stronger but easier to understand conditions). 4. Let , δ, E ∈ (0, 1).

The marginal distribution Q X of Q has a differentiable density that is bounded above and bounded away from 0. 2. The conditional Q-probability distribution Q x of the label y given any object x has a differentiable density qx . 3. Both qx and qx are continuous and bounded uniformly in x. 4. As a function of x, qx (y) is Lipschitz uniformly in y. 5. For each x ∈ X there exists tx such that Q x ({y | qx (y) ≥ tx }) = 1 − . 6. For some δ > 0, the gradient of qx is bounded above and bounded away from 0 uniformly in x ∈ X and y ∈ R satisfying |qx (y) − tx | < δ.

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