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.
Every weak Perron number is realized as a stretch factor of a homeomorphism on a surface.
Tensoring -weak differentiable structures preserves their properties.
The main goal of the present paper is two-fold. First we extend the theory of toroidal embeddings introduced by Kempf, Knudsen, Mumford and Saint-Donat to the class of toroidal varieties with stratifications (which is the main body of the paper). Second we give a proof of the following weak factorization theorem as an …
New theory for PCA under weak latent factors, improving inference and testing.
WeLa-VAE learns interpretable disentangled representations with weak supervision.
In this paper we develop a Morse-like theory in order to decompose birational maps and morphisms of smooth projective varieties defined over a field of characteristic zero into more elementary steps which are locally étale isomorphic to equivariant flips, blow-ups and blow-downs of toric varieties. A crucial role in th…
We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data to come from a single GFA model, we allow latent clusters, each having a different GFA model and producing a different class distribution…
Intangible investment becomes a strong predictor of stock returns over time.
New heat flow for harmonic maps avoids singularities but not bubbles.
The paper develops a method to model high-dimensional data with many variables and weak signals.
KFT improves tensor forecasting by incorporating side information.
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporat…
Recently, researches related to unsupervised disentanglement learning with deep generative models have gained substantial popularity. However, without introducing supervision, there is no guarantee that the factors of interest can be successfully recovered. Motivated by a real-world problem, we propose a setting where …
New method learns useful disentangled representations from weakly labeled data.
Study improves weak error estimates for rough volatility models.
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
W2S FT often outperforms weak teachers due to low intrinsic dimensionality.
New method detects global factors near BBP phase transition in high-dimensional data.
We propose a novel estimation approach for the covariance matrix based on the -regularized approximate factor model. Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness assumption generally adopted for the standard approximate factor…
Building on the work of the fourth author in math.AG/9904074, we prove the weak factorization conjecture for birational maps in characteristic zero: a birational map between complete nonsingular varieties over an algebraically closed field K of characteristic zero is a composite of blowings up and blowings down with sm…
The study measures systemic risk using common and tail dependence factors.
Paper presents a new policy gradient theorem using weak derivatives for reinforcement learning.
New tests for identifying the number of latent factors in short panels with small time dimensions.
Paper tackles offline RL with weak assumptions on both function classes and data coverage.
Study curvature properties of w.a. S-manifolds with new conditions.
Optimal tensor PCA for estimating factors and loadings in high-dimensional panel data.
This paper studies the effect of discretizing the parametrization of a dictionary used for Matching Pursuit decompositions of signals. Our approach relies on viewing the continuously parametrized dictionary as an embedded manifold in the signal space on which the tools of differential (Riemannian) geometry can be appli…
Develops a framework for identifying mispriced assets through attention factors for statistical arbitrage.
Tensor completion requires fewer samples with weak side information.
Conditions for hyperbolic and relatively hyperbolic extensions of free groups using automorphisms with fixed points.
Boosting improves accuracy by combining weak learners into a voting classifier.
We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than , of the variance of the target variables. To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian ap…
Study finds whitepaper narratives do not predict market factor structure.
Tests factor models by decomposing market into body and tail legs, revealing inconsistent results.
Embeds flag manifolds into classical ones, proving rigidity in Kähler geometry.
Paper presents a deep learning method for estimating asset return precision matrices in noisy financial markets.
In dealing with high-dimensional data sets, factor models are often useful for dimension reduction. The estimation of factor models has been actively studied in various fields. In the first part of this paper, we present a new approach to estimate high-dimensional factor models, using the empirical spectral density of …
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and a…
New method decomposes profits and losses continuously, avoiding discrete reporting issues.
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal. Therefore, (1) explaining away the structured noise in multiple-output regression is of paramount importanc…
CP-factorization for high-dimensional tensor time series and double projection iterations
In this paper we will survey some recent developments in the last decade or so on variation of Geometric Invariant Theory and its applications to Birational Geometry such as the weak Factorization Theorems of nonsingular projective varieties and more generally projective varieties with finite quotient singularities. Al…
FASC clusters data with latent factors, improving on naive methods.
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
Real-world datasets are often biased with respect to key demographic factors such as race and gender. Due to the latent nature of the underlying factors, detecting and mitigating bias is especially challenging for unsupervised machine learning. We present a weakly supervised algorithm for overcoming dataset bias for de…
The Dantzig selector has received popularity for many applications such as compressed sensing and sparse modeling, thanks to its computational efficiency as a linear programming problem and its nice sampling properties. Existing results show that it can recover sparse signals mimicking the accuracy of the ideal procedu…
Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art m…
We propose an affine extension of the Linear Gaussian term structure Model (LGM) such that the instantaneous covariation of the factors is given by an affine process on semidefinite positive matrices. First, we set up the model and present some important properties concerning the Laplace transform of the factors and th…