A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study Poincaré inequality in metric spaces via separating sets.
Finite rigid sets found in complex of curves for surfaces.
An explanation is given for the initially surprising ubiquity of separating sets in normal complex surface germs. It is shown that they are quite common in higher dimensions too. The relationship between separating sets and the geometry of the metric tangent cone of Bernig and Lytchak is described. Moreover, separating…
A new measure DCSI quantifies separability for density-based clustering.
Wavesplit separates speech from mixtures using clustering.
We prove that the separated curve complex of a closed orientable surface of genus g is (g-3)-connected. We also obtain a connectivity property for a separated curve complex of the open surface that is obtained by removing a finite set from a closed one, but it is then assumed that the removed set is endowed with a part…
Study on hyperbolic groups, focusing on separability and splittings.
New graph types help identify complex relationships.
A new NMF variant tackles underdetermined problems with sparse and separable assumptions.
New method uses Multiple Choice Learning for speech separation.
Randomly initialized neural networks can linearly separate arbitrary sets.
New complex connects graph separability to group properties.
Finite rigid sets found in surface curve complexes.
New indices for determining cluster compactness and separability.
New concept of regular separation for ODEs leads to improved Hardy field results.
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS). Our formulation is based on the log-linear model equipped with a hierarchically structured sample space, which has theoretical guarantees to uniquely recover a set of source signals by minimizing the K…
Scattering networks maximize separation on low-dimensional data.
We solve minimal separator problems in AMP chain graphs and improve structure learning algorithms.
This paper investigates how data augmentation improves linear separation of manifold data.
Shallow nonlinear networks can separate classes linearly with polynomially scaling width.
Constructs isoperimetric regions from separating hypersurfaces.
DSI measures dataset separability for neural networks.
Study on self-similar sets on Riemannian manifolds with new separation conditions.
We formalize causal separation in portfolio theory, deriving a closed-form projected Markowitz solution.
In this paper, we presented a novel semi-supervised one-class classification algorithm which assumes that class is linearly separable from other elements. We proved theoretically that class is linearly separable if and only if it is maximal by probability within the sets with the same mean. Furthermore, we presented an…
New algorithm AG-OG optimizes separable convex-concave problems efficiently.
The article finds conditions for separating filling pairs on surfaces and constructs a Morse function.
We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by multiplicative factor for the class of projections over the Boolean hypercube o…
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to better capture the structure of tensor data by generalizing the separable dictionary learning model. Two different approaches for learning m…
This work establishes universality for deep equivariant networks, overcoming limitations of previous approaches.
Develops large-sample theory for non-stationary source separation.
Speech separation refers to extracting each individual speech source in a given mixed signal. Recent advancements in speech separation and ongoing research in this area, have made these approaches as promising techniques for pre-processing of naturalistic audio streams. After incorporating deep learning techniques into…
Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separat…
Let M be a hyperbolizable, nontrivial compression body without toroidal boundary components. In this paper, we characterize which discrete and faithful representations of the fundamental group of M into PSL(2,C) are separable-stable. The set of separable-stable representations forms a domain of discontinuity for the ac…
We provide a strengthening of Jordan separation, to the setting of maps from a compact topological space X into a sphere, where the source space X is not necessarily a codimension one sphere, and the map is not necessarily injective.
New algorithm achieves small-loss bounds in online learning with improved rates.
We obtain a finite generating set for the level 2 twist subgroup of the mapping class group of a closed non-orientable surface. The generating set consists of crosscap pushing maps along non-separating two-sided simple loops and squares of Dehn twists along non-separating two-sided simple closed curves. We also prove t…
Recently, a family of tractable NMF algorithms have been proposed under the assumption that the data matrix satisfies a separability condition Donoho & Stodden (2003); Arora et al. (2012). Geometrically, this condition reformulates the NMF problem as that of finding the extreme rays of the conical hull of a finite set …
New conditions ensure MMDs separate and converge to target distributions.
New framework links fractal complexity to separation dimension.
In the curve complex for a surface, a handlebody set is the set of loops that bound properly embedded disks in a given handlebody bounded by the surface. A boundary set is the set of non-separating loops in the curve complex that bound two-sided, properly embedded surfaces. For a Heegaard splitting, the distance betwee…
Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation and hyperspectral unmixing. Given a data matrix and a factorization rank , NMF looks for a nonnegative matrix with columns and a …
Nonnegative matrix factorization (NMF) under the separability assumption can provably be solved efficiently, even in the presence of noise, and has been shown to be a powerful technique in document classification and hyperspectral unmixing. This problem is referred to as near-separable NMF and requires that there exist…
A new method separates data points using entropy minimization over a hypercube.
The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012) turns non-negative matrix factorization (NMF) into a tractable problem. Recently, a new class of provably-correct NMF algorithms have emerged under this assumption. In this paper, we reformulate the separable NMF problem as that of finding the ext…
We prove that the set of orthogonal separable coordinates on an arbitrary (pseudo-)Riemannian manifold carries a natural structure of a projective variety, equipped with an action of the isometry group. This leads us to propose a new, algebraic geometric approach to the classification of orthogonal separable coordinate…
We consider the online multiclass linear classification under the bandit feedback setting. Beygelzimer, Pál, Szörényi, Thiruvenkatachari, Wei, and Zhang [ICML'19] considered two notions of linear separability, weak and strong linear separability. When examples are strongly linearly separable with margin , they prese…
SepVAE separates patient-specific patterns from healthy ones using contrastive VAE.