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.
New method uses correlation-ratio for transfer learning, improving target model inference.
Deep neural networks (DNNs) have achieved exceptional performances in many tasks, particularly, in supervised classification tasks. However, achievements with supervised classification tasks are based on large datasets with well-separated classes. Typically, real-world applications involve wild datasets that include si…
The paper describes a method to infer the signal-to-noise ratio in portfolio optimization.
The ratio of volume to crossing number of a hyperbolic knot is known to be bounded above by the volume of a regular ideal octahedron, and a similar bound is conjectured for the knot determinant per crossing. We investigate a natural question motivated by these bounds: For which knots are these ratios nearly maximal? We…
Paper proposes a method to select base classes for few-shot learning.
Advocates Tversky's model for image similarity learning.
Paper detects changes in graph-based data streams using likelihood-ratios.
Transfer learning assumes classifiers of similar tasks share certain parameter structures. Unfortunately, modern classifiers uses sophisticated feature representations with huge parameter spaces which lead to costly transfer. Under the impression that changes from one classifier to another should be ``simple'', an effi…
RATIO improves neural network robustness and explainability.
Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, KwikCluster in practice re…
Lipschitz equivalence of self-similar sets is an important area in the study of fractal geometry. It is known that two dust-like self-similar sets with the same contraction ratios are always Lipschitz equivalent. However, when self-similar sets have touching structures the problem of Lipschitz equivalence becomes much …
Develops a framework for identifying mispriced assets through attention factors for statistical arbitrage.
The abstract proves polygon inscriptions in curves with specific edge ratios.
Evaluates local explanations using white-box models and log odds ratios.
Double descent in portfolio optimization shows improved performance with complexity, then declines, due to overfitting.
Study detects signals in spiked Wigner models using log likelihood ratio.
The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibl…
Deep learning improves forensic matching of casings.
We define a family of four-point invariants for Shilov boundaries of bounded symmetric domains of tube type, which generalizes the classical four-point cross ratio on the unit circle. This generalization, which is based on a similar construction of Clerc and Ørsted, is functorial and well-behaved under products; these …
Graph-based LRE estimates likelihood-ratios collaboratively for nodes.
Simultaneous orthogonal matching pursuit (SOMP) and block OMP (BOMP) are two widely used techniques for sparse support recovery in multiple measurement vector (MMV) and block sparse (BS) models respectively. For optimal performance, both SOMP and BOMP require \textit{a priori} knowledge of signal sparsity or noise vari…
We discuss - in what is intended to be a pedagogical fashion - a criterion, which is a lower bound on a certain ratio, for when a stock (or a similar instrument) is not a good investment in the long term, which can happen even if the expected return is positive. The root cause is that prices are positive and have skewe…
GAN normalizes CT scans for consistent radiomic feature values.
Proposes a robust similarity measure for sparse time series data.
EB improves asset pricing by mining large strategies without lookahead bias.
Alignment of neural network representations is influenced by SNR and sample size.
The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.
Understanding the exceptional Lie groups as the symmetry groups of simpler objects is a long-standing program in mathematics. Here, we explore one famous realization of the smallest exceptional Lie group, G2. Its Lie algebra acts locally as the symmetries of a ball rolling on a larger ball, but only when the ratio of r…
Unified framework for transfer learning regression without extra cost.
We give asymptotic bounds for the optimal Lipschitz constants for the systole map from the Teichmuller space to the curve complex. We give similar results to those known for closed surfaces in the cases when the genus is fixed or the ratio of genus and punctures is a rational number.
The cognitive framework of conceptual spaces proposes to represent concepts as regions in psychological similarity spaces. These similarity spaces are typically obtained through multidimensional scaling (MDS), which converts human dissimilarity ratings for a fixed set of stimuli into a spatial representation. One can d…
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
Method aligns multilingual news for better stock return prediction.
Recently, deep neural network (DNN) has made a breakthrough in monaural source enhancement. Through a training step by using a large amount of data, DNN estimates a mapping between mixed signals and clean signals. At this time, we use an objective function that numerically expresses the quality of a mapping by DNN. In …
Similar to the definition of Dupin hypersurface in Riemannian space forms, we define the spacelike Dupin hypersurface in Lorentzian space forms. As conformal invariant objects, spacelike Dupin hypersurfaces are studied in this paper using the framework of conformal geometry. Further we classify the spacelike Dupin hype…
The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifest…
A method detects changes in heterogeneous data streams over graph nodes.
MASnet enhances speech on mobile devices with low latency.
The paper establishes criteria for spacetime inextendibility using asymptotic volume-distance-ratio analysis.
This paper studies activation sparsity in large language models, finding key trends and implications.
We investigate scaling and memory effects in return intervals between price volatilities above a certain threshold for the Japanese stock market using daily and intraday data sets. We find that the distribution of return intervals can be approximated by a scaling function that depends only on the ratio between the …
Study Ricci flows on manifolds, proving they behave like self-similar solutions and confirming a conjecture.
Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.
New similarity measure for covariate shift improves nonparametric regression rates.
Study shows surfaces with similar length spectra are smoothly deformable.
In this paper we provide an up-to-date survey on the study of Lipschitz equivalence of self-similar sets. Lipschitz equivalence is an important property in fractal geometry because it preserves many key properties of fractal sets. A fundamental result by Falconer and Marsh [On the Lipschitz equivalence of Cantor sets, …
Develops a deep learning approach for statistical arbitrage.