Davide Bassetti

Edoardo Vecchi, Davide Bassetti, Fabio Graziato et al.

Small data learning problems are characterized by a significant discrepancy between the limited amount of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information, and cannot derive an appropriate learning rule which allows to discriminate between different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the Gauge-Optimal Approximate Learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation and classification problems for small data learning problems. We prove that the optimal solution of the GOAL algorithm consists in piecewise-linear functions in the Euclidean space, and that it can be approximated through a monotonically convergent algorithm which presents -- under the assumption of a discrete segmentation of the feature space -- a closed-form solution for each optimization substep and an overall linear iteration cost scaling. The GOAL algorithm has been compared to other state-of-the-art machine learning (ML) tools on both synthetic data and challenging real-world applications from climate science and bioinformatics (i.e., prediction of the El Nino Southern Oscillation and inference of epigenetically-induced gene-activity networks from limited experimental data). The experimental results show that the proposed algorithm outperforms the reported best competitors for these problems both in learning performance and computational cost.

Davide Bassetti, Lukáš Pospíšil, Michael Groom et al.

Progress of AI has led to very successful, but by no means humble models and tools, especially regarding (i) the huge and further exploding costs and resources they demand, and (ii) the over-confidence of these tools with the answers they provide. Here we introduce a novel mathematical framework for a non-equilibrium entropy-optimizing reformulation of Boltzmann machines based on the exact law of total probability and the exact convex polytope representations. We show that it results in the highly-performant, but much cheaper, gradient-descent-free learning framework with mathematically-justified existence and uniqueness criteria, and cheaply-computable confidence/reliability measures for both the model inputs and the outputs. Comparisons to state-of-the-art AI tools in terms of performance, cost and the model descriptor lengths on a broad set of synthetic and real-world problems with varying complexity reveal that the proposed method results in more performant and slim models, with the descriptor lengths being very close to the intrinsic complexity scaling bounds for the underlying problems. Applying this framework to historical climate data results in models with systematically higher prediction skills for the onsets of important La Niña and El Niño climate phenomena, requiring just few years of climate data for training - a small fraction of what is necessary for contemporary climate prediction tools.

Illia Horenko, Davide Bassetti, Lukáš Pospíšil

Shannon entropy (SE) and its quantum mechanical analogue von Neumann entropy are key components in many tools used in physics, information theory, machine learning (ML) and quantum computing. Besides of the significant amounts of SE computations required in these fields, the singularity of the SE gradient is one of the central mathematical reason inducing the high cost, frequently low robustness and slow convergence of such tools. Here we propose the Fast Entropy Approximation (FEA) - a non-singular rational approximation of Shannon entropy and its gradient that achieves a mean absolute error of $10^{-3}$, which is approximately $20$ times lower than comparable state-of-the-art methods. FEA allows around $50\%$ faster computation, requiring only $5$ to $6$ elementary computational operations, as compared to tens of elementary operations behind the fastest entropy computation algorithms with table look-ups, bitshifts, or series approximations. On a set of common benchmarks for the feature selection problem in machine learning, we show that the combined effect of fewer elementary operations, low approximation error, and a non-singular gradient allows significantly better model quality and enables ML feature extraction that is two to three orders of magnitude faster and computationally cheaper when incorporating FEA into AI tools.