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.
The paper develops a theory for random forests, separating variance components and providing methods for estimating prediction intervals.
Stochastic differential equation approximation for linear TD(0) under Markovian noise
In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of th…
One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings --- e.g., a big shopping mall and a university campus --- is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural network…
Trading floors need to be twice as deep as electronic markets to compete.
Study variance-optimal hedging of forward curve derivatives under stochastic volatility.
Improved analysis for fair federated learning reduces dependence on noise floor.
We study the portfolio selection problem of a long-run investor who is maximising the asymptotic growth rate of her expected utility. We show that, somewhat surprisingly, it is essentially not affected by introduction of a floor constraint which requires the wealth process to dominate a given benchmark at all times. We…
New findings on optimization landscape of Toeplitz covariance estimation.
Improved covariance matrix estimation for portfolio optimization with guaranteed PSD and controlled conditioning.
Consider an agent who enters a financial market on day t = 0 with an initial capital amount x. He invests this amount on stocks and the money market, and by day t = T, has generated a wealth W . He is given a convex class of probability measures (called scenarios) and a real-valued function (or floors) corresponding to…
We present novel empirical observations regarding how stochastic gradient descent (SGD) navigates the loss landscape of over-parametrized deep neural networks (DNNs). These observations expose the qualitatively different roles of learning rate and batch-size in DNN optimization and generalization. Specifically we study…
EdgeLite detects hazardous supermarket floors, improving safety.
Algorithm speeds up search for stationary targets with guaranteed accuracy.
We determine the price of digital double barrier options with an arbitrary number of barrier periods in the Black-Scholes model. This means that the barriers are active during some time intervals, but are switched off in between. As an application, we calculate the value of a structure floor for structured notes whose …
The study uses machine learning to analyze office floor plans and predict function based on geometry.
Enhances ocean floor mapping with adaptive uncertainty estimates.
The paper decomposes unsupervised learning's generalization error into model, data, and variance components.
A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the…
Develops a new framework for perpetual futures on binary prediction markets.
A training-free conformal interval is a mandatory baseline for probabilistic time-series forecasting.
New network approximates functions with error decreasing with network width and depth.
The study bounds exceptional surgeries for hyperbolic knots.
The relationships between braid ordering and the geometry of its closure is studied. We prove that if an essential closed surface in the complements of closed braid has relatively small genus with respect to the Dehornoy floor of the braid, is circular-foliated in a sense of Birman-Menasco's Braid foliation the…
Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can wor…
We construct models for the pricing and risk management of inflation-linked derivatives. The models are rational in the sense that linear payoffs written on the consumer price index have prices that are rational functions of the state variables. The nominal pricing kernel is constructed in a multiplicative manner that …
Three-hidden-layer neural networks can approximate Hölder continuous functions uniformly with exponential rate.
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model…
Language models allocate information storage, not collapsing into uniform representations.
New model estimates indoor radon distribution with higher spatial resolution.
In this article, we propose a new numerical approach to high-dimensional partial differential equations (PDEs) arising in the valuation of exotic derivative securities. The proposed method is extended from Reisinger and Wittum (2007) and uses principal component analysis (PCA) of the underlying process in combination w…
We formalize causal separation in portfolio theory, deriving a closed-form projected Markowitz solution.
Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the retail unit level lacks a theore…
The paper uses Bayesian Surprise to identify unexpected structures in indoor environments.
The genus of knots is a one of the fundamental invariant and can be seen as a complexity of knots. In this paper, we give a lower bound of genus using Dehornoy floor, which is a measure of complexity of braids in terms of braid ordering.
Near-interpolating models grow norms quickly, affecting generalization.
AdaQuantFL reduces communication in federated learning by adaptively quantizing model updates.
A framework for cost of belief revision in uncertain agents.
This paper considers the classification of linear subspaces with mismatched classifiers. In particular, we assume a model where one observes signals in the presence of isotropic Gaussian noise and the distribution of the signals conditioned on a given class is Gaussian with a zero mean and a low-rank covariance matrix.…
Study proposes explainable analytics for manufacturing process planning.
Language models fail to process hallucinated responses, and this study diagnoses the failure.
The main result of this paper that a martingale evolution can be chosen for Libor such that all the Libor interest rates have a common market measure; the drift is fixed such that each Libor has the martingale property. Libor is described using a field theory model, and a common measure is seen to be emerge naturally f…
SAEs struggle with curved activation manifolds, revealing layer-dependent scaling laws.
SREC markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh. Firms offset these certif…
We investigate the application of two heuristic methods, genetic algorithms and tabu/scatter search, to the optimisation of realistic portfolios. The model is based on the classical mean-variance approach, but enhanced with floor and ceiling constraints, cardinality constraints and nonlinear transaction costs which inc…
Fast, reliable, and error-bounded option pricing with neural networks
Optimal portfolio tracking with dynamic capital injection into a ratcheting benchmark.
Cumulant expansion is used to derive accurate closed-form approximation for Monthly Sum Options in case of constant volatility model. Payoff of Monthly Sum Option is based on sum of caped (and probably floored) returns. It is noticed, that can be used as a small parameter in Edgeworth expansion. First …