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 introduces a new outer measure for continuous price paths with instant enforcement.
The paper proves ML estimators are strongly consistent for identifying edge weights in BAR models.
Given a Lorentzian manifold , a geodesic in and a timelike Jacobi field along , we introduce a special class of instants along that we call -pseudo conjugate (or focal relatively to some initial orthogonal submanifold). We prove that the -pseudo conjugate insta…
In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration of the duality between online predic…
We introduce a new tool for predicting the evolution of an option for the cases where at some specific time, there is a high-degree of uncertainty for identifying its price. We work over the special case where we can predict the evolution of the system by joining a single price for the Option, defined at some specific …
Using symplectic techniques and spectral analysis of smooth paths of self-adjoint operators, we characterize the set of conjugate instants along a geodesic in an infinite dimensional Riemannian Hilbert manifold.
HyperFast instantly classifies tabular data in one pass.
In this paper, we propose a classification based glottal closure instants (GCI) detection from pathological acoustic speech signal, which finds many applications in vocal disorder analysis. Till date, GCI for pathological disorder is extracted from laryngeal (glottal source) signal recorded from Electroglottograph, a d…
In the present paper we construct stock price processes with the same marginal log-normal law as that of a geometric Brownian motion and also with the same transition density (and returns' distributions) between any two instants in a given discrete-time grid. We then illustrate how option prices based on such processes…
In this work we determine bifurcation instants for 1-parameter families of solutions to the Yamabe problem defined on maximal flag manifolds. We also study the local rigidity points, namely, a isolated solution of the Yamabe problem.
The paper tackles the problem of deriving a topological structure among stock prices from high frequency historical values. Similar studies using low frequency data have already provided valuable insights. However, in those cases data need to be collected for a longer period and then they have to be detrended. An effec…
The paper tackles fairness in forecasting and learning linear dynamical systems.
In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. In particular, we propose an approach to provably determine the time instant from which the new/changed features start becoming relevant with…
ALINE optimizes Bayesian inference and data acquisition by strategically querying informative data.
Panoptic trades options without oracles on Ethereum.
PINN-FEM combines PINNs and FEM for accurate Dirichlet boundary condition enforcement.
Deep learning optimizes VWAP strategy for lower transaction costs.
FLAP adapts policies quickly to new tasks using shared linear representations.
The study examines if enforcing fairness helps mitigate bias due to demographic shifts.
Measures of implied volatility roughness corrected for bias.
By appealing to renewal theory we determine the equations that the mean exit time of a continuous-time random walk with drift satisfies both when the present coincides with a jump instant or when it does not. Particular attention is paid to the corrections ensuing from the non-Markovian nature of the process. We show t…
SenSeI ensures fair models by enforcing invariance on sensitive groups.
POLICE enforces linear constraints on deep neural networks efficiently.
A number of machine learning (ML) methods have been proposed recently to maximize model predictive accuracy while enforcing notions of group parity or fairness across sub-populations. We propose a desirable property for these procedures, slack-consistency: For any individual, the predictions of the model should be mono…
Study dynamic portfolio choice under rotating drivers, revealing a new geometric structure.
New algorithms detect anomalies in processes with minimal delay.
GER learns particle dynamics from unpaired snapshots using physics-informed GANs.
COSMIC identifies LTV systems from large data sets efficiently.
Develops a method to quantify racial bias in law enforcement systems.
This paper explores how enforcing equivariance constraints limits neural network expressivity and proposes compensatory model size increases.
New algorithms reduce orthogonality constraint enforcement time in machine learning.
Enforces physical constraints in GP regression models.
Let f:Σ_1 --> Σ_2 be a map between compact Riemannian manifolds of constant curvature. This article considers the evolution of the graph of f in the product of Σ_1 and Σ_2 by the mean curvature flow. Under suitable conditions on the curvature of Σ_1 and Σ_2 and the differential of the initial map, we show that the flow…
Enforcing distributions of latent variables in neural networks is an active subject. It is vital in all kinds of generative models, where we want to be able to interpolate between points in the latent space, or sample from it. Modern generative AutoEncoders (AE) like WAE, SWAE, CWAE add a regularizer to the standard (d…
In this paper, we present Gamma-LSTM, an enhanced long short term memory (LSTM) unit, to enable learning of hierarchical representations through multiple stages of temporal abstractions. Gamma memory, a hierarchical memory unit, forms the central memory of Gamma-LSTM with gates to regulate the information flow into var…
Develops a deep learning method for enforcing no-arbitrage in local volatility surfaces.
Study finds bifurcation and local rigidity points for solutions to the Yamabe problem on Aloff-Wallach Spaces.
Gamification enhances law enforcement training on terrorism financing.
We study two time-scale linear stochastic approximation algorithms, which can be used to model well-known reinforcement learning algorithms such as GTD, GTD2, and TDC. We present finite-time performance bounds for the case where the learning rate is fixed. The key idea in obtaining these bounds is to use a Lyapunov fun…
VCAE improves autoencoder quality on MNIST and CelebA.
A statistical generalization is made of microeconomics in the spirit of going from classical to statistical mechanics. The price and quantity of every commodity1 traded in the market, at each instant of time, is considered to be an independent random variable: all prices and quantities are considered to be stochastic p…
We argue that the word ``critical'' in the title is not purely literary. Based on our and other previous work on nonlinear complex dynamical systems, we summarize present evidence, on the Oct. 1929, Oct. 1987, Oct. 1987 Hong-Kong, Aug. 1998 global market events and on the 1985 Forex event, for the hypothesis advanced f…
Study shows physical drift affects put-call parity enforcement, not just option payoffs.
Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption. We propose Monte Carlo methods to quantize the weights and activations of pre-trained neural networks without any re-training. By performing importance sampling we obtain quantiz…
New method uses MMD estimators to enforce model invariance with missing data.
We develop an adversarial-reinforcement learning scheme for microswimmers in statistically homogeneous and isotropic turbulent fluid flows, in both two (2D) and three dimensions (3D). We show that this scheme allows microswimmers to find non-trivial paths, which enable them to reach a target on average in less time tha…
Task offloading is a promising technology to exploit the benefits of fog computing. An effective task offloading strategy is needed to utilize the computational resources efficiently. In this paper, we endeavor to seek an online task offloading strategy to minimize the long-term latency. In particular, we formulate a s…
Study tests how U.S. equity prices align with global asset frequencies using financial variables.