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.
Projective simulation converges to optimal behavior in Markov decision processes.
Teaching tool simplifies Monte Carlo simulation for project risk analysis.
A new method prioritizes project risks using Monte Carlo Simulation.
Paper introduces a new project control method using Monte Carlo and statistical learning.
The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and prese…
Enhances multi-project scheduling with multiple priority rules.
Paper proposes a method to estimate project cost contingency reserves considering various types of uncertainty.
A new inference method using regression and batched discrepancies.
Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory. The model has been shown to provide a flexible framework for constructing reinforcement-learning agents, and it allows for quantum mechanical generalization, which leads to a speed-up in deliberat…
We introduce cylindrical projections to simulate infinite-dimensional occupation flows of diffusions.
New simulation model predicts financial market dynamics with high accuracy.
Simulates realistic execution and costs in limit order books.
A method for dynamic portfolio choice with uncertain parameters using Pontryagin projection.
We present an algorithm for converting an indoor spherical panorama into a photograph with a simulated overhead view. The resulting image will have an extremely wide field of view covering up to 4π steradians of the spherical panorama. We argue that our method complements the stereographic projection commonly used in t…
New method selects variables for GP regression using sparse projection.
Graph Prolongation Convolutional Networks improve model performance in microtubule bending simulations.
Quantum methods model uncertain volatility in financial markets.
This is a detailed tutorial paper which explains the Principal Component Analysis (PCA), Supervised PCA (SPCA), kernel PCA, and kernel SPCA. We start with projection, PCA with eigen-decomposition, PCA with one and multiple projection directions, properties of the projection matrix, reconstruction error minimization, an…
Molecular simulations produce very high-dimensional data-sets with millions of data points. As analysis methods are often unable to cope with so many dimensions, it is common to use dimensionality reduction and clustering methods to reach a reduced representation of the data. Yet these methods often fail to capture the…
PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotated data is the most sensitive to distributional changes, where sensitivity is defined by the Hellinger distance between distributions before…
Deployment of emerging technologies and rapid change in industries has created a lot of risk for initiating the new projects. Many techniques and suggestions have been introduced but still lack the gap from various prospective. This paper proposes a reliable project scheduling approach. The objectives of project schedu…
An algorithm preserves topological features in dimensionality reduction.
We use Karhunen-Loève expansion for efficient pricing of exotic derivatives.
We introduce sparse random projection, an important dimension-reduction tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections. Subsequently, estimation …
A new framework for dimension reduction using ensemble of random projections.
A common challenge in nonparametric inference is its high computational complexity when data volume is large. In this paper, we develop computationally efficient nonparametric testing by employing a random projection strategy. In the specific kernel ridge regression setup, a simple distance-based test statistic is prop…
Lattice investment projects support process model with corruption is formulated and analyzed. The model is based on the Ising lattice model of ferromagnetic but takes deal with the social phenomenon. Set of corruption agents is considered. It is supposed that agents are placed in sites of the lattice. Agents take decis…
Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice different task environments are best handled by different learning models, rather than a single, universal, approach. Most non-trivial models thus require the adjustment of several to many learnin…
In this work we are concerned with valuing optionalities associated to invest or to delay investment in a project when the available information provided to the manager comes from simulated data of cash flows under historical (or subjective) measure in a possibly incomplete market. Our approach is suitable also to inco…
Simulates DeLend Platform behavior to optimize operational parameters.
Approximate inference via information projection has been recently introduced as a general-purpose approach for efficient probabilistic inference given sparse variables. This manuscript goes beyond classical sparsity by proposing efficient algorithms for approximate inference via information projection that are applica…
The simulator is an R package that streamlines the process of performing simulations by creating a common infrastructure that can be easily used and reused across projects. Methodological statisticians routinely write simulations to compare their methods to preexisting ones. While developing ideas, there is a temptatio…
Paper studies PSGD for constrained optimization problems and its statistical properties.
Betas are possibly the most frequently applied tool to analyze how securities relate to the market. While in very widespread use, betas only express dynamics derived from second moment statistics. Financial returns data often deviate from normal assumptions in the sense that they have significant third and fourth order…
Network analysis improves risk assessment for surety bonds.
A new method improves stochastic gradient descent for faster and more efficient estimation.
PCA outperforms random projections in retaining second order signals from latent groups.
Study predicts wind energy potential in Gulf of Oman using climate models.
For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets. We illustrate our theoretical results with simulation…
Interesting data often concentrate on low dimensional smooth manifolds inside a high dimensional ambient space. Random projections are a simple, powerful tool for dimensionality reduction of such data. Previous works have studied bounds on how many projections are needed to accurately preserve the geometry of these man…
A fast method for estimating radar amplitude density parameters.
The paper proposes a method to monitor deep learning predictions for retraining, reducing costs.
The paper develops a method for optimal projection selection in high-dimensional classification.
Sharp-SSL uses random projections to identify important variables for semi-supervised learning.
Paper reduces turbomachinery CFD simulations by identifying key dimensions.
Method improves simulation accuracy by mitigating distribution shift in hybrid systems.
Empirical study finds IT project costs follow a power-law distribution, exposing risk underestimation.
We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that LOCO obtains a solution which …