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 uses Apple ML to accurately detect and classify lung cancer.
FLAIR dataset for federated learning benchmarks.
In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approximate Path for Penalized Likelihood Estimators. Both the convex penalty (such as LASSO) and the nonc…
Study apple tasting feedback in online binary classification, providing new insights into minimax expected mistakes.
A deterministic apple tasting learner is developed, confirming a conjecture and providing tight bounds for mistake bounds.
The paper uses data science to predict stock trends of Amazon, Apple, Google, and Microsoft.
We consider the problem of maximizing expected power utility from consumption over an infinite horizon in the Black-Scholes model with proportional transaction costs, as studied in Shreve and Soner [Ann. Appl. Probab. 4 (1994) 609-692]. Similar to Kallsen and Muhle-Karbe [Ann. Appl. Probab. 20 (2010) 1341-1358], we der…
The present paper deals with the characterization of no-arbitrage properties of a continuous semimartingale. The first main result, Theorem \refMainTheoremCharNA, extends the no-arbitrage criterion by Levental and Skorohod [Ann. Appl. Probab. 5 (1995) 906-925] from diffusion processes to arbitrary continuous semimartin…
The purpose of this article is to provide, with the help of a fluctuation identity, a generic link between a number of known identities for the first passage time and overshoot above/below a fixed level of a Levy process and the solution of Gerber and Shiu [Astin Bull. 24 (1994) 195-220], Boyarchenko and Levendorskii […
Capsule networks improve performance on image classification tasks with fewer parameters.
Smart watches can identify smoking gestures with high accuracy.
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
Modeling stock price fluctuations using Brownian motion and stochastic differential equations.
We show that the shortfall risk of binomial approximations of game (Israeli) options converges to the shortfall risk in the corresponding Black--Scholes market considering Lipschitz continuous path-dependent payoffs for both discrete- and continuous-time cases. These results are new also for usual American style option…
GANs can be used to extract Fisher vectors for unsupervised feature learning.
We prove a conjecture formulated by Pablo M. Chacon and Guillermo A. Lobos in [Pseudo-parallel Lagrangian submanifolds in complex space forms, Differential Geom. Appl.] stating that every Lagrangian pseudo-parallel submanifold of a complex space form of dimension at least 3 is semi-parallel.
We present an axiomatic/synthetic account of the Huygens Principle of wave fronts. The primitive notions are "touching", and (a weak notion of ) metric. The paper simplifies some of the exposition of the author's "Metric spaces and SDG", Theory and Appl. of Categories 32 (2017), 803-822
Amortizes MIPS by training neural networks to predict optimal keys.
The paper mentioned in the title introduces the entropic value at risk. I give some extra comments and using the general theory make a relation with some commonotone risk measures.
Barrieu, Rouault, and Yor [J. Appl. Probab. 41 (2004)] determined asymptotics for the logarithm of the distribution function of the Hartman-Watson distribution. We determine the asymptotics of the density. This refinement can be applied to the pricing of Asian options in the Black-Scholes model.
The paper analyzes robustness and sensitivity of rough Volterra stochastic volatility models.
TADA improves diffusion sampling without training, up to 186% faster.
Introduces an asymmetric model for measuring market risk.
Using the result by D.Gessler (Differential Geom. Appl. 7 (1997) 303-324, DIPS-9/98, http://diffiety.ac.ru/preprint/98/09_98abs.htm), we show that any invariant variational bivector (resp., variational 2-form) on an evolution equation with nondegenerate right-hand side is Hamiltonian (resp., symplectic).
We introduce a model for the short-term dynamics of financial assets based on an application to finance of quantum gauge theory, developing ideas of Ilinski. We present a numerical algorithm for the computation of the probability distribution of prices and compare the results with APPLE stocks prices and the S&P500 ind…
We confirm the square-root law of market impact on Apple Inc. using a large dataset.
New model improves recommendation systems by analyzing user-item interactions.
MLSys aims to bridge ML and systems research.
Let be either a Bernoulli random walk or a Brownian motion with drift, and let , . This paper solves the general optimal prediction problem \sup_{0\leqτ\leq T}\sE[f(M_T-B_τ)], where the supremum is over all stopping times adapted to the natural…
Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engineering. A plethora of ML applications transform human lives a…
We use the energy gap result of pure Yang-Mills equation [Feehan P.M.N., Adv. Math. 312 (2017), 547-587, arXiv:1502.00668] to prove another energy gap result of complex Yang-Mills equations [Gagliardo M., Uhlenbeck K., J. Fixed Point Theory Appl. 11 (2012), 185-198, arXiv:1401.7366], when Riemannian manifold of dim…
The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics thereupon. In order to run ML algorithms at such scales, on a distributed clust…
System detects overfitting in ML apps, improving quality and efficiency.
SelfReflect measures LLM uncertainty by summarizing belief distribution.
MLPerf benchmarks ML inference systems across diverse hardware.
This paper studies iteration convergence of Kronecker graphical lasso (KGLasso) algorithms for estimating the covariance of an i.i.d. Gaussian random sample under a sparse Kronecker-product covariance model and MSE convergence rates. The KGlasso model, originally called the transposable regularized covariance model by …
The DoD needs a robust process to evaluate AI/ML model performance and robustness.
A review of ML and DL for ecological data analysis.
In this article, we give the non-integrated defect relations for the Gauss map of a complete minimal surface with finite total curvature in This is a continuation of previous work of Ha-Trao [J. Math. Anal. Appl., \textbf{430} (2015), 76-84.], which we extend here to targets of higher dimension.
MLPerf benchmarks ML training to drive performance improvements.
AI methods are energy-intensive, but efficiency alone isn't enough for sustainability.
A rigorous ML pipeline for binary classification in biomedical studies, focusing on pancreatic cancer.
Research evaluates model extraction attacks on complex ML models and introduces a defense.
This paper tackles hidden technical debts in fair ML systems for Fintech.
ALMANACS benchmarks explainability methods on simulatability.
This paper emphasizes the need for uncertainty quantification in data-driven ML models for nuclear engineering.
Bucketed PCA-NN outperforms DNNs by 96% on MNIST.
New method prevents entropy collapse in Transformer training, leading to more stable and robust models.