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 binary choice with asymmetric loss, offering simple solutions.
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.
A new asymmetric contrastive loss improves performance on imbalanced datasets.
BAEN-SVM improves SVM robustness to noisy data.
Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.
We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.
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.
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…
A new PU classifier PUAL tackles trifurcate data issues.
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…
New SVM model balances sparsity and robustness in noisy data.
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…
Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes i…
Model captures asymmetric extreme events in financial returns.
Novel model improves clinical risk prediction by transferring knowledge between tasks over time.
The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.
Loss-calibrated EP improves Bayesian decision-making by focusing on utility-sensitive posterior approximations.
R2T hybrid model improves robust regression for asymmetric noise.
Optimal insurance contracts are designed to screen risk preferences and risk types under asymmetric information.
The study improves VaR forecast accuracy by modeling conditional quantile dynamics.
Supervised learning is an active research area, with numerous applications in diverse fields such as data analytics, computer vision, speech and audio processing, and image understanding. In most cases, the loss functions used in machine learning assume symmetric noise models, and seek to estimate the unknown function …
A new privacy-preserving deep learning scheme for asymmetrically collaborative machine learning.
DEUA detects diffusion-generated images by accounting for different types of uncertainty.
Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to each variable in an unregularized way. The ensemble quantile classifier is a more flexible regularize…
In an economy with asymmetric information, the smart contract in the blockchain protocol mitigates uncertainty. Since, as a new trading platform, the blockchain triggers segmentation of market and differentiation of agents in both the sell and buy sides of the market, it recomposes the asymmetric information and genera…
By incorporating market impact and asymmetric sensitivity into the evolutionary minority game, we study the coevolutionary dynamics of stock prices and investment strategies in financial markets. Both the stock price movement and the investors' global behavior are found to be closely related to the phase region they fa…
SIGTRON improves classification accuracy for imbalanced datasets.
Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be…
Study of geometric analysis on asymmetric metric spaces, including heat flow and Sobolev spaces.
This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support ou…
A new convex loss function optimizes set predictions with balanced size and coverage.
This work analyzes the maximum-margin bias in quasi-homogeneous neural networks.
New metrics for Anosov representations defined from Thurston's asymmetric metrics.
Paper tackles robust matrix completion with heavy-tailed noise.
Generalizes Thurston's asymmetric metric to flat metrics.
We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the loss. This holds true even for non-smooth, non-convex losses and in any RKHS. The first term is a (kernel) mean operator --the …
This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.
Theoretical justification for asymmetric actor-critic algorithms in reinforcement learning.
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…
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…
In this work we investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. The main aim of our paper is to extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Based on existing literat…
A robust loss for anomaly mitigation and unsupervised contamination classification