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.
This paper simplifies ANS for statisticians, making it easier to use.
Method extracts governing laws from non-Gaussian stochastic systems data.
Develops a method for lossless compression using latent variable models.
New asymmetric kernel methods improve feature learning.
In this paper, we propose a novel asymmetric -insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the -insensitive zone approach in it but, they fail to extend the -insensitive approach for quantile estimation in true sense. The propo…
This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put tra…
R2T hybrid model improves robust regression for asymmetric noise.
This paper develops a new methodology for studying continuous-time Nash equilibrium in a financial market with asymmetrically informed agents. This approach allows us to lift the restriction of risk neutrality imposed on market makers by the current literature. It turns out that, when the market makers are risk averse,…
Innovative extensions to option pricing models using asymmetric Brownian motion and random walk approaches.
Algorithm stabilizes queues in asymmetric systems with unknown service rates.
Study wormholes in Einstein-Yang-Mills theory with a phantom field.
Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.
New friction model for geometric locomotion systems.
Gradient descent recovers principal components of overparametrized asymmetric matrices without explicit regularization.
We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.
New HMC method uses asymmetrical momentum distributions and improves performance.
The importance of collateralization through the change of funding cost is now well recognized among practitioners. In this article, we have extended the previous studies of collateralized derivative pricing to more generic situation, that is asymmetric and imperfect collateralization with the associated counter party c…
One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represented as a numeric table. This methodology has been used in the developme…
Dynamic Vocabulary Pruning stabilizes LLM training by removing low-probability tokens.
Silkswap models stablecoin trading with minimal price impact.
We study cascades on a two-layer multiplex network, with asymmetric feedback that depends on the coupling strength between the layers. Based on an analytical branching process approximation, we calculate the systemic risk measured by the final fraction of failed nodes on a reference layer. The results are compared with…
New guarantees for asymmetric sketching in compressive learning.
We study systems of Brownian particles on the real line, which interact by splitting the local times of collisions among themselves in an asymmetric manner. We prove the strong existence and uniqueness of such processes and identify them with the collections of ordered processes in a Brownian particle system, in which …
We analyze a simple asset transfer model in which the transfer amount is a fixed fraction of the giver's wealth. The model is analyzed in a new way by Laplace transforming the master equation, solving it analytically and numerically for the steady-state distribution, and exploring the solutions for various values o…
Complex systems are typically represented by large ensembles of observations. Correlation matrices provide an efficient formal framework to extract information from such multivariate ensembles and identify in a quantifiable way patterns of activity that are reproducible with statistically significant frequency compared…
A new privacy-preserving deep learning scheme for asymmetrically collaborative machine learning.
Study proposes a data-driven CBR system for improved bankruptcy prediction.
Gaussian process framework learns interaction kernels in multi-species particle systems.
Study of geometric analysis on asymmetric metric spaces, including heat flow and Sobolev spaces.
This work improves polynomial approximations for functions with asymmetric behavior.
The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still an open problem. Bits-Back with Asymmetric Numeral Systems (BB-A…
Background: For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not…
New metrics for Anosov representations defined from Thurston's asymmetric metrics.
In this work we study an economic agent based model under different asymmetric information degrees. This model is quite simple and can be treated analytically since the buyers evaluate the quality of a certain good taking into account only the quality of the last good purchased plus her perceptive capacity β. As a cons…
Generalizes Thurston's asymmetric metric to flat metrics.
This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.
Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users. The Shapley framework for explainability has strength in its general applicability combined with its precise, rigorous foundation: it provides a common, model-agnostic language for AI e…
Theoretical justification for asymmetric actor-critic algorithms in reinforcement learning.
This work presents deep asymmetric networks with a set of node-wise variant activation functions. The nodes' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive. As a result, features learned by the nodes are sorted by the node indices…
With the recent development in mobile computing devices and as the ubiquitous deployment of access points(APs) of Wireless Local Area Networks(WLANs), WLAN based indoor localization systems(WILSs) are of mounting concentration and are becoming more and more prevalent for they do not require additional infrastructure. A…
We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. Howev…
We present a large scale hyperbolic recommender system. We discuss why hyperbolic geometry is a more suitable underlying geometry for many recommendation systems and cover the fundamental milestones and insights that we have gained from its development. In doing so, we demonstrate the viability of hyperbolic geometry f…
The article confirms two quasi-alternating surgeries for 9 asymmetric L-space knots.
Mix2FLD improves FL accuracy with FD, reducing convergence time.
Asymmetric expansion preserves convexity in hyperbolic geometry.
The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.
Optimizes risk assessment tools using mixed-integer programming.
We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…