Inductive randomness predictors
This paper introduces inductive randomness predictors, which form a superset of inductive conformal predictors. Its focus is on a very simple special case, binary inductive randomness predictors. It is interesting that binary inductive randomness predictors have an advantage over inductive conformal predictors, although they also have a serious disadvantage. This advantage will allow us to reach the surprising conclusion that non-trivial inductive conformal predictors are inadmissible in the sense of statistical decision theory.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Universality of conformal prediction under the assumption of randomness
Conformal predictors provide set or functional predictions that are valid under the assumption of randomness, i.e., under the assumption of independent and identically distributed data. The question asked in this paper i…
Conformal PredictionPredictionregressionvalidMulti-class probabilistic classification using inductive and cross Venn-Abers predictors
Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems. We present a new approach to multi-class probability estima…
Binary ClassificationClassificationClassifier calibrationConformal Prediction+1Conformal Predictors for Compound Activity Prediction
The paper presents an application of Conformal Predictors to a chemoinformatics problem of identifying activities of chemical compounds. The paper addresses some specific challenges of this domain: a large number of comp…
Activity PredictionConformal PredictionPredictionPAC-Bayesian Transportation Bound
Empirically, the PAC-Bayesian analysis is known to produce tight risk bounds for practical machine learning algorithms. However, in its naive form, it can only deal with stochastic predictors while such predictors are ra…
Conditional independence testing under misspecified inductive biases
Conditional independence (CI) testing is a fundamental and challenging task in modern statistics and machine learning. Many modern methods for CI testing rely on powerful supervised learning methods to learn regression f…
regression