161 citations · 493 across the 15 of their papers we have counts for
17 papers
Calibrated Large Language Models for Binary Question Answering
Patrizio Giovannotti, Alexander Gammerman
Quantifying the uncertainty of predictions made by large language models (LLMs) in binary text classification tasks remains a challenge. Calibration, in the context of LLMs, refers…
Conformal testing: binary case with Markov alternatives
Vladimir Vovk, Ilia Nouretdinov, Alex Gammerman
We continue study of conformal testing in binary model situations. In this note we consider Markov alternatives to the null hypothesis of exchangeability. We propose two new classe…
Protected probabilistic classification
Vladimir Vovk, Ivan Petej, Alex Gammerman
This paper proposes a way of protecting probabilistic prediction models against changes in the data distribution, concentrating on the case of classification and paying particular…
Retrain or not retrain: Conformal test martingales for change-point detection
Vladimir Vovk, Ivan Petej, Ilia Nouretdinov +3
We argue for supplementing the process of training a prediction algorithm by setting up a scheme for detecting the moment when the distribution of the data changes and the algorith…
Computationally efficient versions of conformal predictive distributions
Vladimir Vovk, Ivan Petej, Ilia Nouretdinov +2
Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather…
Conformal calibrators
Vladimir Vovk, Ivan Petej, Paolo Toccaceli +1
Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation…