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 MBO scheme for data clustering is analyzed in the large data limit, proving convergence to optimal partition problems.
Efficient algorithm for clustering and classification using MBO scheme.
Paper uses MBO data for high-frequency price forecasting.
We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative. Our method relies on a graph-based …
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unreliable approximations. We propose a novel entropy-based MBO called Pare…
Conformal Candidate Certification advances offline MBO by certifying candidate designs with statistical guarantees.
Study validates metrics for offline MBO using diffusion models.
New method optimizes complex models with minimal data, proving global optimality.
Networks capture pairwise interactions between entities and are frequently used in applications such as social networks, food networks, and protein interaction networks, to name a few. Communities, cohesive groups of nodes, often form in these applications, and identifying them gives insight into the overall organizati…
We present two graph-based algorithms for multiclass segmentation of high-dimensional data. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex, with the fun…
Project forecasts liquidity withdrawal using machine learning models.
The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples tha…
ClusterLOB clusters market events to identify different trading behaviors.
Theoretical proof shows COMs are a type of contrastive divergence model with improved sampling.
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi…
Poisson learning improves graph-based semi-supervised learning at very low label rates.
Paper proposes COM-QEL to avoid overoptimistic solutions in offline optimization.
A new method for incorporating preferences in multi-objective Bayesian optimization.
New probabilistic scheme combines deep learning with Runge-Kutta methods for solving PDEs.
It was proved in 1998 by Ben-David and Litman that a concept space has a sample compression scheme of size d if and only if every finite subspace has a sample compression scheme of size d. In the compactness theorem, measurability of the hypotheses of the created sample compression scheme is not guaranteed; at the same…
Study SL(2,C) character schemes for finitely generated groups.
A new method for computing image curvature efficiently and accurately.
Reduces multiclass and regression compression schemes to binary ones.
Study evaluates UK CDC schemes, finding intergenerational cross-subsidies in flat-accrual schemes and dynamic-accrual schemes can reduce but not eliminate them.
We study first-order optimization methods obtained by discretizing ordinary differential equations (ODEs) corresponding to Nesterov's accelerated gradient methods (NAGs) and Polyak's heavy-ball method. We consider three discretization schemes: an explicit Euler scheme, an implicit Euler scheme, and a symplectic scheme.…
JaxMARL-HFT accelerates MARL for HFT with 240x speedup.
Generalizes soft noncommutative schemes to flag varieties.
AES scheme improves Bermudan and American option pricing for Heston models.
In this paper we propose a new kind of high order numerical scheme for backward stochastic differential equations(BSDEs). Unlike the traditional -scheme, we reduce truncation errors by taking carefully for every subinterval according to the characteristics of integrands. We give error estimates of this nonlinear…
Defines hypercomplex analytic spaces and schemes.
We extend the scheme developed in B. Düring, A. Pitkin, "High-order compact finite difference scheme for option pricing in stochastic volatility jump models", 2019, to the so-called stochastic volatility with contemporaneous jumps (SVCJ) model, derived by Duffie, Pan and Singleton. The performance of the scheme is asse…
New schemes for SDEs on manifolds keep solutions close to the manifold.
Vector fields on schemes have flows if rings are finitely generated.
In this paper we propose a generalized numerical scheme for backward stochastic differential equations(BSDEs). The scheme is based on approximation of derivatives via Lagrange interpolation. By changing the distribution of sample points used for interpolation, one can get various numerical schemes with different stabil…
Extends JKO scheme for iterative algorithms with unknown parameters.
A new scheme for FBSDEs simplifies computation without Monte Carlo.
A discretization scheme for nonnegative diffusion processes is proposed and the convergence of the corresponding sequence of approximate processes is proved using the martingale problem framework. Motivations for this scheme come typically from finance, especially for path-dependent option pricing. The scheme is simple…
Study on statistical estimation over Gaussian MAC, comparing analog and digital schemes.
Efficient simulation scheme for rough Heston model reduces computational cost.
Study finds risk management significantly improves pension scheme efficiency in Kenya.
In this paper, we propose an acceleration scheme for online memory-limited PCA methods. Our scheme converges to the first eigenvectors in a single data pass. We provide empirical convergence results of our scheme based on the spiked covariance model. Our scheme does not require any predefined parameters such as t…
In the present paper, we introduce a numerical scheme for the price of a barrier option when the price of the underlying follows a diffusion process. The numerical scheme is based on an extension of a static hedging formula of barrier options. For getting the static hedging formula, the underlying process needs to have…
The paper analyzes convergence of Riemannian SA schemes for stochastic optimization.
New CDC scheme avoids intergenerational subsidies, offering better outcomes.
We introduce (binary) Darboux transformation for general differential equation of the second order in two independent variables. We present a discrete version of the transformation for a 6-point difference scheme. The scheme is appropriate to solving a hyperbolic type initial-boundary value problem. We discuss several …
We present a new high-order compact scheme for the multi-dimensional Black-Scholes model with application to European Put options on a basket of two underlying assets. The scheme is second-order accurate in time and fourth-order accurate in space. Numerical examples confirm that a standard second-order finite differenc…
We develop high-order approximations for the Heston model.
A risk of small defined-benefit pension schemes is that there are too few members to eliminate idiosyncratic mortality risk, that is there are too few members to effectively pool mortality risk. This means that when there are few members in the scheme, there is an increased risk of the liability value deviating signifi…