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.
Study of geometric analysis on asymmetric metric spaces, including heat flow and Sobolev spaces.
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…
Theoretical justification for asymmetric actor-critic algorithms in reinforcement 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…
Proposes a method to generate prediction intervals using weighted asymmetric loss functions.
Study binary choice with asymmetric loss, offering simple solutions.
Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suffers from the requirements of additional network components and the limited scalability to a large number of tasks. We propose a novel appro…
We highlight several analogies between the Finsler (infinitesimal) properties of Teichmüller's metric and Thurston's asymmetric metric on Teichmüller space. Thurston defined his asymmetric metric in analogy with Teichmüllers' metric, as a solution to an extremal problem, which consists, in the case of the asymmetric me…
In recent years, correntropy has been seccessfully applied to robust adaptive filtering to eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables. This definition is reasonable when the error between the two random…
A new activation function improves credit scoring accuracy for imbalanced datasets.
Local asymptotic minimax risk bounds in a locally asymptotically mixture of normal family of distributions have been investigated under asymmetric loss functions and the asymptotic distribution of the optimal estimator that attains the bound has been obtained.
This paper studies gradient flows in asymmetric metric spaces and proves existence results.
BAEN-SVM improves SVM robustness to noisy data.
Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.
Recent studies show that a negative shock in stock prices will generate more volatility than a positive shock of similar magnitude. The aim of this paper is to appraise the hypothesis under which the conditional mean and the conditional variance of stock returns are asymmetric functions of past information. We compare …
The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.
Study proves value of non-Markovian games with partial, asymmetric info.
We initiate the rigorous study of classification in quasi-metric spaces. These are point sets endowed with a distance function that is non-negative and also satisfies the triangle inequality, but is asymmetric. We develop and refine a learning algorithm for quasi-metrics based on sample compression and nearest neighbor…
Innovative extensions to option pricing models using asymmetric Brownian motion and random walk approaches.
We consider classification in the presence of class-dependent asymmetric label noise with unknown noise probabilities. In this setting, identifiability conditions are known, but additional assumptions were shown to be required for finite sample rates, and so far only the parametric rate has been obtained. Assuming thes…
Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectures that SGD with proper configuration is able to find wide and flat local minima, which have been proposed to be associated with good gener…
The {\em drawdown} process of a completely asymmetric Lévy process is equal to reflected at its running supremum : . In this paper we explicitly express in terms of the scale function and the Lévy measure of the law of the sextuple of the first-passage time of over the leve…
This work improves polynomial approximations for functions with asymmetric behavior.
New framework improves differential privacy for asymmetric datasets.
New asymmetric metric on Teichmüller space for surfaces.
We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.
Leasing is a popular channel to market new cars. Pricing a leasing contract is complicated because the leasing rate embodies an expectation of the residual value of the car after contract expiration. To aid lessors in their pricing decisions, the paper develops resale price forecasting models. A peculiarity of the leas…
We investigate how asymmetrizing an impurity function affects the choice of optimal node splits when growing a decision tree for binary classification. In particular, we relax the usual axioms of an impurity function and show how skewing an impurity function biases the optimal splits to isolate points of a particular c…
New metrics for Anosov representations defined from Thurston's asymmetric metrics.
Generalizes Thurston's asymmetric metric to flat metrics.
This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.
A new PU classifier PUAL tackles trifurcate data issues.
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…
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a …
New asymmetric kernel methods improve feature learning.
The article confirms two quasi-alternating surgeries for 9 asymmetric L-space knots.
Asymmetric expansion preserves convexity in hyperbolic geometry.
The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.
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…
In this paper we show how the study of asymmetric R&D alliances, that are those between young and small firms and large and MNEs firms for knowledge exploration and/or exploitation, requires the adoption of a coopetitive framework which consider both collaboration and competition. We draw upon the literature on asymmet…
We introduce an asymmetric distance function, which we call the `left Hausdorff distance function', on the space of geodesic laminations on a closed hyperbolic surface of genus at least 2. This distance is an asymmetric version of the Hausdorff distance between compact subsets of a metric space. We prove a rigidity res…
COMET Flows model multivariate extremes with heavy tails and asymmetric dependence.
DEUA detects diffusion-generated images by accounting for different types of uncertainty.
Bayesian VI copula models capture asymmetric intraday equity dependence.
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,…
New SVM model balances sparsity and robustness in noisy data.
This work describes compactifications of metric spaces and vector spaces using asymmetric norms.
The study improves VaR forecast accuracy by modeling conditional quantile dynamics.