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.
Paper tackles unfair advantages in DARTS, presenting Fair DARTS to improve neural architecture search.
We introduce an irreversible discrete multiplicative process that undergoes Bose-Einstein condensation as a generic model of competition. New players with different abilities successively join the game and compete for limited resources. A player's future gain is proportional to its ability and its current gain. The the…
In this paper we study the problem of acoustic scene classification, i.e., categorization of audio sequences into mutually exclusive classes based on their spectral content. We describe the methods and results discovered during a competition organized in the context of a graduate machine learning course; both by the st…
We study the dynamics of co-evolution of producers and customers described by bit-strings representing individual traits. Individual ''size-like'' properties are controlled by binary encounters which outcome depends upon a recognition process. Depending upon the parameter set-up, mutual selection of producers and custo…
PieClam autoencodes graphs into communities, improving graph anomaly detection.
Research quantifies financial exclusion risks in UK, focusing on cash infrastructure and socio-economic factors.
Many practical applications such as gene expression analysis, multi-task learning, image recognition, signal processing, and medical data analysis pursue a sparse solution for the feature selection purpose and particularly favor the nonzeros \emph{evenly} distributed in different groups. The exclusive sparsity norm has…
Introduces joint exclusivity (JE), a new form of negative dependence.
We consider the symmetric exclusion process on suitable random grids that approximate a compact Riemannian manifold. We prove that a class of random walks on these random grids converge to Brownian motion on the manifold. We then consider the empirical density field of the symmetric exclusion process and prove that it …
Extends conformal prediction to contrastive learning for better coverage of positive samples.
Exclusive Group Lasso improves feature selection in correlated biological data.
Study cooperative bandit learning with imperfect communication, achieving near-optimal performance.
Exclusive Lasso improves survival prediction in cancer datasets.
New method selects variables in groups with few nonzeros, improving support recovery.
The paper proposes a model to learn disentangled representations using mutual information.
ETM identifies field-specific keywords in text classification.
An exclusion particle model is considered as a highly simplified model of a limit order market. Its price behavior reproduces the well known crossover from over-diffusion (Hurst exponent H>1/2) to diffusion (H=1/2) when the time horizon is increased, provided that orders are allowed to be canceled. For early times a ma…
Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and government (ESG) problems. They create their investment exclusion lists by gathering information from variou…
Spaces of polynomials are shown to be Euclidean balls.
New study analyzes security of neural network data reconstruction attacks.
Generative Flow Networks solve shortest path problems in graphs.
MOVDA improves skill ratings by considering margin of victory deviations.
An ongoing challenge in the analysis of document collections is how to summarize content in terms of a set of inferred themes that can be interpreted substantively in terms of topics. The current practice of parametrizing the themes in terms of most frequent words limits interpretability by ignoring the differential us…
Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which is more robust and discriminative. However, most AE based methods only focus on the reconstruction within the encoder-decoder phase, which ig…
Improved flow-based inference speeds up and boosts accuracy for complex simulations.
Analyzes how inclusion/exclusion from STOXX Europe 600 Index affects company prices.
Study risk-constrained Kelly optimization for mutually exclusive outcomes, proving support invariance and developing a structured algorithm.
Study of SO(3)-irreducible geometry in complex 5D and ternary Pauli exclusion principle.
TempoPFN models for zero-shot time series forecasting using synthetic data.
In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term…
Meta-learning improved by using information theory to prioritize data-driven adaptation.
The availability of large microarray data has led to a growing interest in biclustering methods in the past decade. Several algorithms have been proposed to identify subsets of genes and conditions according to different similarity measures and under varying constraints. In this paper we focus on the exclusive row bicl…
Hexagonal norm double bubble problem solved with minimal configurations.
The exclusive or (xor) function is one of the simplest examples that illustrate why nonlinear feedforward networks are superior to linear regression for machine learning applications. We review the xor representation and approximation problems and discuss their solutions in terms of probabilistic logic and associative …
Paper adapts CVS method for TL in functional linear regression.
Valid causal inference with invalid instruments using majority or modal valid relationships.
Modeling wind dynamics in Saudi Arabia using deep learning and stochastic PDEs.
Continuing the quest for exclusive Racah matrices, which are needed for evaluation of colored arborescent-knot polynomials in Chern-Simons theory, we suggest to extract them from a new kind of a double-evolution -- that of the antiparallel double-braids, which is a simple two-parametric family of two-bridge knots, gene…
Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classificatio…
CREAM models enable concept-grounded predictions and interpretability.
It is well known that a random vector with given marginal distributions is comonotonic if and only if it has the largest sum with respect to the convex order [ Kaas, Dhaene, Vyncke, Goovaerts, Denuit (2002), A simple geometric proof that comonotonic risks have the convex-largest sum, ASTIN Bulletin 32, 71-80. Cheung (2…
Mapping spaces of supermanifolds are usually thought as exclusively in functorial terms (i.e. trough the Grothendieck functor of points). In this work we provide a geometric description of such mapping spaces in terms of infinite-dimensional super-vector bundles.
Network-based strategy for optimal cryptocurrency portfolios identified.
New algorithm tackles multi-player bandit problems with limited access to arms.
AutoML system boosts interpretability without sacrificing performance.
New method finds infinitely many knots in 3-manifolds.
This paper is a next step in the project of systematic description of colored knot and link invariants started in previous papers. In this paper, we managed to explicitly find the inclusive Racah matrices, i.e. the whole set of mixing matrices in channels with all possible $…
We derive valuations of a portfolio of financial instruments from a securities lending perspective, under different assumptions, and show a weighting scheme that converges to the true valuation. We illustrate conditions under which our alternative weighting scheme converges faster to the true valuation when compared to…