World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
32
Citations
7365
World Ranking
9504
National Ranking
599

Overview

Alexander Gammerman is affiliated with Royal Holloway University of London in the United Kingdom. Their research primarily falls within the field of Computer Science, with a focus on subfields like Artificial Intelligence, Statistics and Probability, General Health Professions, Statistics, Probability and Uncertainty, and Signal Processing.

The main topics in Gammerman's body of work include Statistical Methods and Inference, Data Stream Mining Techniques, Neural Networks and Applications, Gaussian Processes and Bayesian Inference, Time Series Analysis and Forecasting, Bayesian Modeling and Causal Inference, and Bayesian Methods and Mixture Models.

Frequent publication venues for Gammerman's work include arXiv (Cornell University) and Pattern Recognition. Some of their recent papers are:

  • Retrain or not retrain: Conformal test martingales for change-point detection (2021), arXiv (Cornell University)
  • Special Issue on Conformal and Probabilistic Prediction with Applications: Preface (2022), Pattern Recognition
  • Conformal testing: binary case with Markov alternatives (2021), arXiv (Cornell University)
  • Protected probabilistic classification (2021), arXiv (Cornell University)
  • Calibrated Large Language Models for Binary Question Answering (2024), arXiv (Cornell University)

Gammerman's frequent co-authors include Vladimir Vovk, Ilia Nouretdinov, Ivan Petej, Marco Cristani, and Matteo Fontana.

Best Publications

  • Algorithmic Learning in a Random World

    Vladimir Vovk;Alex Gammerman;Glenn Shafer

  • Ridge Regression Learning Algorithm in Dual Variables

    Craig Saunders;Alexander Gammerman;Volodya Vovk

  • Learning by transduction

    A. Gammerman;V. Vovk;V. Vapnik

  • Inductive Confidence Machines for Regression

    Harris Papadopoulos;Kostas Proedrou;Volodya Vovk;Alexander Gammerman

  • Algorithmic Learning in a Random World

    Unknown

  • Machine-Learning Applications of Algorithmic Randomness

    Volodya Vovk;Alexander Gammerman;Craig Saunders

  • Transductive Confidence Machines for Pattern Recognition.

    Kostas Proedrou;Ilia Nouretdinov;Volodya Vovk;Alexander Gammerman

  • Transduction with Confidence and Credibility

    Craig Saunders;Alexander Gammerman;Volodya Vovk

  • Transductive confidence machines for pattern recognition

    Kostas Proedrou;Ilia Nouretdinov;Volodya Vovk;Alexander Gammerman

  • Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

    Ilia Nouretdinov;Sergi G. Costafreda;Alexander Gammerman;Alexey Ya. Chervonenkis

  • Hedging Predictions in Machine Learning

    Alexander Gammerman;Vladimir Vovk

  • Sequence alignment kernel for recognition of promoter regions.

    Leo Gordon;Alexey Ya. Chervonenkis;Alex J. Gammerman;Ilham A. Shahmuradov

  • Support vector regression with ANOVA decomposition kernels

    Mark O. Stitson;Alex Gammerman;Vladimir Vapnik;Volodya Vovk

  • Regression conformal prediction with nearest neighbours

    Harris Papadopoulos;Vladimir Vovk;Alex Gammerman

  • Support vector density estimation

    Jason Weston;Alex Gammerman;Mark O. Stitson;Vladimir Vapnik

  • Machine-learning algorithms for credit-card applications

    R. H. Davis;D. B. Edelman;A. J. Gammerman

  • Prediction algorithms and confidence measures based on algorithmic randomness theory

    Alex Gammerman;Volodya Vovk

  • On-line predictive linear regression

    Vladimir Vovk;Ilia Nouretdinov;Alex Gammerman

  • Probabilistic reasoning in evidential assessment

    C.G.G. Aitken;A.J. Gammerman

  • Criteria of Efficiency for Conformal Prediction

    Vladimir Vovk;Valentina Fedorova;Ilia Nouretdinov;Alexander Gammerman

  • Reliable Confidence Measures for Medical Diagnosis With Evolutionary Algorithms

    A Lambrou;H Papadopoulos;A Gammerman

  • Testing exchangeability on-line

    Vladimir Vovk;Ilia Nouretdinov;Alex Gammerman

  • Criteria of efficiency for conformal prediction

    Vladimir Vovk;Ilia Nouretdinov;Valentina Fedorova;Ivan Petej

Frequent Co-Authors

Usha Menon
Usha Menon University College London
Ian Jacobs
Ian Jacobs University of New South Wales
Rainer Cramer
Rainer Cramer University of Reading
Mike Waterfield
Mike Waterfield Ludwig Cancer Research
Vladimir Vapnik
Vladimir Vapnik Princeton University
Jaakko Astola
Jaakko Astola Tampere University
Andrew N. Nicolaides
Andrew N. Nicolaides Imperial College London
Victor V. Solovyev
Victor V. Solovyev Royal Holloway University of London
Constantinos S. Pattichis
Constantinos S. Pattichis University of Cyprus
Ola Blixt
Ola Blixt University of Copenhagen

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