Reliable Diagnostics by Conformal Predictors

The talk reviews a modern machine learning technique called Conformal Predictors.

The talk outlines the basic ideas of Conformal Predictors and then illustrates the technique with applications to several medical problems.

Large-scale Probabilistic Prediction With and Without Validity Guarantees

The topic of this talk is a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity, is computationally efficient, and preserves predictive efficiency.

Intelligent Learning: Similarity Control and Knowledge Transfer

The common observation was that human students require much less examples for training than a learning machine. It is because the human students have an intelligent teacher and that teacher-student interactions are based not only on the brute force methods of function estimation from observations.

Conformal Prediction and its Applications

In this talk, Prof. Gammerman will introduce the concept of Conformal Prediction and its applications. In particular, a comparison of Conformal Prediction and Bayesian approach will be discussed.

Treatment of Uncertainty in the Foundations of Probability

Game-theoretic probability formalizes the picture in which both risk and uncertainty interfere at every moment. This picture will be demonstrated by open theories in science and the emergence of stochasticity and probability in finance.

​Brute Force and ​Intelligent ​Methods of Learning

The goal of this talk is to introduce a new conceptual model of learning, the so-called Intelligent Learning, which goes beyond the classical model.

Entropy Properties of a Decision Rule Class in Connection with ML abilities

This talk was delivered by Prof. Alexey Chervonenkis in Slovenia, 2007.

​Rethinking Statistical Learning Theory: Learning Using Statistical Invariants

In the talk I will consider Teacher-Student interaction in learning processes. I will introduce a new learning paradigm, called Learning Using Statistical Invariants (LUSI).

Randomness and Non-determinism

This talk was delivered by Prof. Leonid Levin for the Kolmogorov Lecture 2004.

The Universal Distribution and Machine Learning

This talk was delivered by Prof. Ray Solomonoff for the Kolmogorov Lecture 2003.

Similarity Control and Knowledge Transfer

This talk was delivered by Prof. Vladimir Vapnik at SLDS 2015 conference.

Memorial Concert Celebrating the Life of Alexey Chervonenkis

This concert was presented to celebrate the life of Alexey Chervonenkis

Commemorative Talks in memory of Alexey Chervonenkis

This talks summarises the applications of data analysis and machine learning, developed by Alexey Chervonenkis.