arXiv research

Optimal portfolios are found for a wide range of utility functions under hyperbolic returns.

Two pension funds mutually insure against longevity risk.

We derive properties of the cdf of random variables defined as saddle-type points of real valued continuous stochastic processes. This facilitates the derivation of the first-order asymptotic properties of tests for stochastic spanning given some stochastic dominance relation. We define the concept of Markowitz stochas…

The paper finds optimal ways to combine ETFs to minimize costs for investors.

We show that the mutual fund theorems of Merton (1971) extend to the problem of optimal investment to minimize the probability of lifetime ruin. We obtain two such theorems by considering a financial market both with and without a riskless asset for random consumption. The striking result is that we obtain two-fund the…

Study on MMV in jump-diffusion models resolves MV's non-monotonicity issues.

The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.

Investigates how rebalancing frequency and transaction costs affect log-optimal portfolios.

New graph types help identify complex relationships.

This paper improves universal sound separation using sound classification.

Neural networks can separate non-separable data using feature maps.

DSI measures dataset separability for neural networks.

Separates estimation and control in risk-sensitive investment problems with partial observation.

Bayesian approach uses generative models as priors for better source separation.

New examples show some convex-cocompact subgroups are separable.

Logarithmic separation profile in hyperbolic groups shows hierarchical structure.

New machine learning method detects quantum separability in large-scale systems.

The study shows subgroup separability conditions for specific groups.

The paper examines subgroup separability for surface and virtual braid groups.

The study of random surfaces reveals asymptotic lengths of separating geodesics.

We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which trains on multi-channel mixtures and learns to project spectrogram bins to source clusters that correlate with various spatial features. We sho…

A new measure DCSI quantifies separability for density-based clustering.

A new NMF variant tackles underdetermined problems with sparse and separable assumptions.

This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on lo…

One approach to monitoring a dynamic system relies on decomposition of the system into weakly interacting subsystems. An earlier paper introduced a notion of weak interaction called separability, and showed that it leads to exact propagation of marginals for prediction. This paper addresses two questions left open by t…

Develops large-sample theory for non-stationary source separation.

Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, singer identification, and voice conversion. In this paper, we propose an intuitive spectrogram-based model for source separation by adapting U-…

Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow sigmoid-type neural networks that achieve 100% accuracy in classification for datasets following a linear separability condition. The separab…

Criterion for subgroup separability in outer automorphism groups.

Study on hyperbolic groups, focusing on separability and splittings.

This paper proposes a new evaluation metric and boosting method for weight separability in neural network design. In contrast to general visual recognition methods designed to encourage both intra-class compactness and inter-class separability of latent features, we focus on estimating linear independence of column vec…

The paper solves optimal bounds for separating data points in high dimensions.

Wavesplit separates speech from mixtures using clustering.

Suppose that all hyperbolic groups are residually finite. The following statements follow: In relatively hyperbolic groups with peripheral structures consisting of finitely generated nilpotent subgroups, quasiconvex subgroups are separable; Geometrically finite subgroups of non-uniform lattices in rank one symmetric sp…

The paper introduces toric separable geometries and finds new extremal metrics.

Singing voice separation attempts to separate the vocal and instrumental parts of a music recording, which is a fundamental problem in music information retrieval. Recent work on singing voice separation has shown that the low-rank representation and informed separation approaches are both able to improve separation qu…

Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems based on deep learning are currently the most successful approaches for solving the underdetermined separation problem, where there are more …

Adaptive algorithm reduces regret in causal bandits.

Improved speech separation and enhancement using neural beamforming.

Study optimizes fairness in predictive models by balancing utility and separation.

Paper introduces a new time separation function for $C^0$ spacetimes.

A new neural network separates vocals from music accompaniment.

Improved mistake bound for group linear separable cases in online multiclass linear classification.

Study on diagonal and separating coordinates for symmetric spaces of rank 1.

Single-microphone, speaker-independent speech separation is normally performed through two steps: (i) separating the specific speech sources, and (ii) determining the best output-label assignment to find the separation error. The second step is the main obstacle in training neural networks for speech separation. Recent…

A tensor field generates separation of variables for certain metrics.

Constructs orthogonal coordinates in curved spaces.

Categorical d-separation criterion simplifies probability graph analysis.