Learning Algebraic Varieties from Samples

We seek to determine a real algebraic variety from a fixed finite subset of points. Existing methods are studied and new methods are developed. Our focus lies on aspects of topology and algebraic geometry, such as dimension and defining polynomials. All algorithms are tested on a range of datasets and made available in a Julia package

Minimax estimation of the mode of functional data

Wir untersuchen den Modalwert einer Verteilung, die auf einem Funktionenraum wie etwa dem Raum integrierbarer Funktionen definiert ist. Die Definition des Modalwerts basiert auf Small-Ball-Wahrscheinlichkeiten. Wir benutzen Entropiemethoden wie etwa endliche Überdeckungen für die Definition eines Modalwertschätzers und die Beschreibung seines asymptotischen Verhaltens. Wir zeigen die starke Konsistenz und ermitteln die optimale Konvergenzrate für eine Klasse von Verteilungen, deren Modalwerte in einer totalbeschränkten Teilmenge des Funktionenraums liegen.We investigate the mode of a distribution defined on a function space, e.g. the space of integrable functions. We give a definition of the mode using small ball probabilities. We use entropy methods, e.g. finite covers, to define an estimator of the mode and to deduce its asymptotic behaviour. We show strong consistency and continue to derive the optimal rate of convergence over a class of distributions whose modes are contained in a totally bounded subset of the function space

Sketching for Large-Scale Learning of Mixture Models

Learning parameters from voluminous data can be prohibitive in terms of memory and computational requirements. We propose a "compressive learning" framework where we estimate model parameters from a sketch of the training data. This sketch is a collection of generalized moments of the underlying probability distribution of the data. It can be computed in a single pass on the training set, and is easily computable on streams or distributed datasets. The proposed framework shares similarities with compressive sensing, which aims at drastically reducing the dimension of high-dimensional signals while preserving the ability to reconstruct them. To perform the estimation task, we derive an iterative algorithm analogous to sparse reconstruction algorithms in the context of linear inverse problems. We exemplify our framework with the compressive estimation of a Gaussian Mixture Model (GMM), providing heuristics on the choice of the sketching procedure and theoretical guarantees of reconstruction. We experimentally show on synthetic data that the proposed algorithm yields results comparable to the classical Expectation-Maximization (EM) technique while requiring significantly less memory and fewer computations when the number of database elements is large. We further demonstrate the potential of the approach on real large-scale data (over 10 8 training samples) for the task of model-based speaker verification. Finally, we draw some connections between the proposed framework and approximate Hilbert space embedding of probability distributions using random features. We show that the proposed sketching operator can be seen as an innovative method to design translation-invariant kernels adapted to the analysis of GMMs. We also use this theoretical framework to derive information preservation guarantees, in the spirit of infinite-dimensional compressive sensing