36 citations · 66 across the 7 of their papers we have counts for
7 papers
Practice of Efficient Data Collection via Crowdsourcing at Large-Scale
Alexey Drutsa, Viktoriya Farafonova, Valentina Fedorova +3
Modern machine learning algorithms need large datasets to be trained. Crowdsourcing has become a popular approach to label large datasets in a shorter time as well as at a lower co…
Latent Distribution Assumption for Unbiased and Consistent Consensus Modelling
Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
We study the problem of aggregation noisy labels. Usually, it is solved by proposing a stochastic model for the process of generating noisy labels and then estimating the model par…
Aggregation of pairwise comparisons with reduction of biases
Nadezhda Bugakova, Valentina Fedorova, Gleb Gusev +1
We study the problem of ranking from crowdsourced pairwise comparisons. Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous…
Criteria of efficiency for conformal prediction
Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova +2
We study optimal conformity measures for various criteria of efficiency of classification in an idealised setting. This leads to an important class of criteria of efficiency that w…
Large-scale probabilistic predictors with and without guarantees of validity
Vladimir Vovk, Ivan Petej, Valentina Fedorova
This paper studies theoretically and empirically a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity (per…
From conformal to probabilistic prediction
Vladimir Vovk, Ivan Petej, Valentina Fedorova
This paper proposes a new method of probabilistic prediction, which is based on conformal prediction. The method is applied to the standard USPS data set and gives encouraging resu…
Plug-in martingales for testing exchangeability on-line
Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov +1
A standard assumption in machine learning is the exchangeability of data, which is equivalent to assuming that the examples are generated from the same probability distribution ind…