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.
Machine learning models predict which ideas will be innovated based on subjective perspectives.
Study uses FDA to analyze discount functions of different temperaments.
Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships …
Emotions play an important role in people's life. Understanding and recognising is not only important for interpersonal communication, but also has promising applications in Human-Computer Interaction, automobile safety and medical research. This project focuses on extending the emotion recognition database, and traini…
Framework learns dynamic graph attributes and links co-evolution.
Multimodal analysis assesses job interview performance and provides feedback.
We evaluate the hedging performance of a high-order compact finite difference scheme from [4] for option pricing in Bates model. We compare the scheme's hedging performance to standard finite difference methods in different examples. We observe that the new scheme outperforms a standard, second-order central finite dif…
ADS explains object differences by quantifying and removing underlying properties.
Proposes Population Difference Criterion for visually observed subpopulation differences.
Typically options with a path dependent payoff, such as Target Accumulation Redemption Note (TARN), are evaluated by a Monte Carlo method. This paper describes a finite difference scheme for pricing a TARN option. Key steps in the proposed scheme involve tracking of multiple one-dimensional finite difference solutions,…
Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.
The paper analyzes how the one-dimensional Wasserstein distance captures pointwise density differences in finite samples.
We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions. The method operates by finding the projection that maximizes the Wasserstein divergence between the resulting univariate populations. Relying on the Cramer-Wold device, it requires no assumptions about th…
A new method for feature fusion in U-Net decoders using difference-based gating.
Manifolds uniquely identified by boundary distance differences.
SDD improves DD for estimating treatment effects by adjusting for confounding.
Study of 2d gauged linear sigma models to derive difference equations and spectral data.
Study on tilings of the plane with two types of tiles of varying areas.
We introduce a framework to study the effective objectives at different time scales of financial market microstructure. The financial market can be regarded as a complex adaptive system, where purposeful agents collectively and simultaneously create and perceive their environment as they interact with it. It has been s…
Study Berry connections for 2d GLSMs, linking to cohomology theories.
Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These t…
This work examines the sensitivity of energy distance to mean differences compared to covariance differences.
Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with…
This study reveals efficient finite-difference computation for gradient regularization in deep learning.
Ghost points affect stability in finite difference schemes for diffusion equations.
MB-DQN uses different backup lengths for improved reinforcement learning.
Develops a method to estimate network difference in high-dimensional time series data.
We study the space of linear difference equations with periodic coefficients and (anti)periodic solutions. We show that this space is isomorphic to the space of tame frieze patterns and closely related to the moduli space of configurations of points in the projective space. We define the notion of combinatorial Gale tr…
We prove that the N-colored Jones polynomial for the torus knot T_{s,t} satisfies the second order difference equation, which reduces to the first order difference equation for a case of T_{2,2m+1}. We show that the A-polynomial of the torus knot can be derived from this difference equation. Also constructed is a q-hyp…
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights. Is there some correspondence between these neural network solutions? For linear networks, it has been shown that different instances of the same network architecture encode the same representatio…
We prove that functions defined on a lattice in a finite dimensional torus with bounded finite differences can be smoothly extended to the whole torus, and relate the bounds on the extension's derivatives with bounds on the original function's finite differences.
Temporal difference learning explained through gradient splitting, improving convergence times.
Since the debut of Evolution Strategies (ES) as a tool for Reinforcement Learning by Salimans et al. 2017, there has been interest in determining the exact relationship between the Evolution Strategies gradient and the gradient of a similar class of algorithms, Finite Differences (FD).(Zhang et al. 2017, Lehman et al. …
Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user grou…
This research explores how different discrete diffusion kernels affect graph generation quality.
Different optimizer choices lead to different financial model predictions.
Introduces a new geometry based on difference angles, showing unique properties.
Researchers develop methods to prevent GANs from generating certain types of images.
We introduce and compare new variability measures based on risk quantiles.
Bayesian methods improve DiD analysis for ATT estimation.
The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the VIX (volatility index US…
Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows rela…
New method for pricing options in stochastic volatility models.
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats. Predicting infection from symptom data is critical, though using symptom data from varied studies in aggregate is challenging because the data is collected in diff…
A new protocol corrects confounding effects to measure alignment-induced activation shifts accurately.
We give infinitely many examples of 2-bridge knots for which the topological and smooth slice genera differ. The smallest of these is the 12-crossing knot . These also provide the first known examples of alternating knots for which the smooth and topological genera differ.
Examines different approaches to Poisson structures in Banach spaces.
We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, such a two-step procedure does not necessarily work well because the first step is performed without …