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 analyzes the dynamics of tokens in transformer models at moderate interaction levels.
Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …
Method for understanding heterogeneous treatment effects in complex causal graphs.
Proposes a two-stage method for testing variable interactions with FDR control.
The paper models financial markets and real economy interactions using a large agent framework.
Study shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.
Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian methods for discovering pairwise interactions enjoy many benefits such as coherent uncertainty quantification, the ability to incorporate b…
A deterministic system of interacting agents is considered as a model for economic dynamics. The dynamics of the system is described by a coupled map lattice with near neighbor interactions. The evolution of each agent results from the competition between two factors: the agent's own tendency to grow and the environmen…
Proposes a method for interpreting time-varying causal effect moderation in high-dimensional data.
We present two Bayesian procedures to infer the interactions and external currents in an assembly of stochastic integrate-and-fire neurons from the recording of their spiking activity. The first procedure is based on the exact calculation of the most likely time courses of the neuron membrane potentials conditioned by …
Enhances content moderation with culturally-aware models.
New lower bounds show challenges in clustering in moderate dimensions.
Variable selection for models including interactions between explanatory variables often needs to obey certain hierarchical constraints. The weak or strong structural hierarchy requires that the existence of an interaction term implies at least one or both associated main effects to be present in the model. Lately, thi…
Optimizes variance reduction in Heston model using large and moderate deviations.
Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…
We extend previous large deviations results for the randomised Heston model to the case of moderate deviations. The proofs involve the Gärtner-Ellis theorem and sharp large deviations tools.
Optimal learning via moderate deviations theory improves statistical accuracy.
Computes invariants distinguishing between immersions and embeddings of doodles and blobs on surfaces.
New method interprets deep learning for causal effects, separating prognostic and moderating covariates.
We provide a unifying treatment of pathwise moderate deviations for models commonly used in financial applications, and for related integrated functionals. Suitable scaling allows us to transfer these results into small-time, large-time and tail asymptotics for diffusions, as well as for option prices and realised vari…
SPINEX improves clustering with explainable neighbors, outperforming other methods.
Unified approach to stochastic Volterra systems' deviations.
Stochastic Gradient Descent shows directional bias with moderate learning rates, impacting optimization outcomes.
We consider call option prices in diffusion models close to expiry, in an asymptotic regime ("moderately out of the money") that interpolates between the well-studied cases of at-the-money options and out-of-the-money fixed-strike options. First and higher order small-time moderate deviation estimates of call prices an…
A new graph-based clustering method for moderate-dimensional data.
We employ a 2x3 factorial experiment to study two central factors in the design of prediction markets (PMs) for idea evaluation: the overall design of the PM, and the elasticity of market prices set by a market maker. The results show that 'multi-market designs' on which each contract is traded on a separate PM lead to…
Introduces Causal Energy Minimization to understand Transformer layers.
Node-link diagrams are a popular method for representing graphs that capture relationships between individuals, businesses, proteins, and telecommunication endpoints. However, node-link diagrams may fail to convey insights regarding graph structures, even for moderately sized data of a few hundred nodes, due to visual …
Paper optimizes change-point detection using learned distributions from training sequences.
Far-from-equilibrium models of interacting particles in one dimension are used as a basis for modelling the stock-market fluctuations. Particle types and their positions are interpreted as buy and sell orders placed on a price axis in the order book. We revisit some modifications of well-known models, starting with the…
Derives scaling limits and fluctuations for SGD in high dimensions.
Random forest (RF) missing data algorithms are an attractive approach for dealing with missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings. Currently there are …
A new tree-based estimator, FastPD, efficiently estimates PD functions for machine learning models.
Proposes a new machine learning-based method for conjoint analysis.
This study examines the interaction between CDS and stock indices, revealing significant short and long-term impacts.
Q-learning with cSMART data assesses cAI tailoring variables.
We propose a novel kinetic exchange model differing from previous ones in two main aspects. First, the basic dynamics is modified in order to represent economies where immediate wealth exchanges are carried out, instead of reshufflings or uni-directional movements of wealth. Such dynamics produces wealth distributions …
A framework for optimizing prompt selection in generative language models.
Cluster analysis of very high dimensional data can benefit from the properties of such high dimensionality. Informally expressed, in this work, our focus is on the analogous situation when the dimensionality is moderate to small, relative to a massively sized set of observations. Mathematically expressed, these are the…
Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performan…
A framework uses proxies to prioritize treatment without estimating causal effects.
We discuss a general dynamic replication approach to counterparty credit risk modeling. This leads to a fundamental jump-process backward stochastic differential equation (BSDE) for the credit risk adjusted portfolio value. We then reduce the fundamental BSDE to a continuous BSDE. Depending on the close out value conve…
Houdini finds high-dimensional saddle points under few constraints.
Study describes frequencies of geodesics on hyperbolic surfaces as genus grows.
The reproducing kernel Hilbert space (RKHS) embedding of distributions offers a general and flexible framework for testing problems in arbitrary domains and has attracted considerable amount of attention in recent years. To gain insights into their operating characteristics, we study here the statistical performance of…
The Lax-Hopf formula simplifies the value function of an intertemporal optimization (infinite dimensional) problem associated with a convex transaction-cost function which depends only on the transactions (velocities) of a commodity evolution: it states that the value function is equal to the marginal fonction of a fin…
Gradient descent learns ReLU functions with non-zero bias efficiently.
Study liquidity provision in decentralized exchanges considering risk aversion and replication costs.