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High-dimensional separability for one- and few-shot learning
New stochastic separation theorems for data with fine-grained structure are formulated and proved.
General stochastic separation theorems with optimal bounds
To manage errors and analyze vulnerabilities, the stochastic separation theorems should evaluate the probability that the dataset will be Fisher separable in given dimensionality and for a given class of distributions.
On the solution uniqueness in portfolio optimization and risk analysis
We consider the issue of solution uniqueness for portfolio optimization problem and its inverse for asset returns with a finite number of possible scenarios.
Augmented Artificial Intelligence: a Conceptual Framework
We combine some ideas of learning in heterogeneous multiagent systems with new and original mathematical approaches for non-iterative corrections of errors of legacy AI systems.
