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.
A framework for eliciting utility functions from investor preferences.
Optimal unimodal fitting for linear loss functions in a sequential, efficient manner.
We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the fitting function. The proposed algorithm generalizes the recent algorithm in the literature, aggregate and iterative disaggregate (AID), whi…
Spectrahedral regression fits convex functions via a non-convex optimization problem.
OccamNet finds interpretable symbolic fits to data efficiently.
JAXFit speeds up curve fitting on GPUs.
The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genetic algorithms have been used successfully for many problems, one criticism is that hand-crafting its fitness function, the test that aims to…
Study evaluates different mathematical models for three case studies using statistical fitting.
Improved estimators for causal inference using cross-fitting and undersmoothing.
Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance fu…
Paper proposes fitting loss functions to data using source functions from information geometry.
Paper solves best approximation by exponential functions for economic data.
Neural networks fit fewer samples than their parameters suggest in practice.
We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the test determines how likely it is that these were generated from a target density function. The measure of goodness-of-fit is a divergence constructed via Stein's method using functions from a Reproducing Kernel Hilbert Space. O…
FORE evaluates occupancy ratios without requiring Bellman completeness.
The paper fits a seven-parameter GTS distribution to financial data.
Establish C^{1,2} regularity of American value functions in Heston model
We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range. We fit saturating splines to data using a convex optimization problem over a space of measures, which we solve using an efficient algorithm based on the condi…
A new method treats all variables equally in fitting data.
The Black-Scholes theory of option pricing has been considered for many years as an important but very approximate zeroth-order description of actual market behavior. We generalize the functional form of the diffusion of these systems and also consider multi-factor models including stochastic volatility. Daily Eurodoll…
Paper uses DRL to improve volatility fitting in equity derivatives.
Efficient offline reinforcement learning with neural networks using differentiable function approximation.
Generative model learns functional vector fields for pharmacokinetics.
Employing profits data of Japanese companies in 2002 and 2003, we identify the non-Gibrat's law which holds in the middle profits region. From the law of detailed balance in all regions, Gibrat's law in the high region and the non-Gibrat's law in the middle region, we kinematically derive the profits distribution funct…
FVI method calculates bicausal OT with neural networks, outperforming other methods.
New neural network models for complex functional data analysis.
Sobolev training helps neural nets fit function values and derivatives.
Gaussian process regression loses locality in high dimensions, affecting molecular energy surface fitting.
A new method reduces the time needed for Bayesian optimization by a factor of 10-100.
Develops monotone tree-based GAMI models using XGBoost.
In Divide & Recombine (D&R), big data are divided into subsets, each analytic method is applied to subsets, and the outputs are recombined. This enables deep analysis and practical computational performance. An innovate D\&R procedure is proposed to compute likelihood functions of data-model (DM) parameters for big dat…
The paper introduces flat-topped PDFs for better fitting machine learning models.
KBB algorithm reduces sample complexity for policy evaluation in general state spaces.
The aim of the present article is to treat the Greek public debt issue strictly as a curve fitting problem. Thus, based on Eurostat data and using the Mathematica technical computing software, an exponential function that best fits the data is determined modelling how the Greek public debt expands with time. Exploring …
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward function limits this repertoire and imposes a manual engineering burden. Self-supervised agents that set their own goals can automate this pr…
This article describes a multivariate polynomial regression method where the uncertainty of the input parameters are approximated with Gaussian distributions, derived from the central limit theorem for large weighted sums, directly from the training sample. The estimated uncertainties can be propagated into the optimal…
A new method for exponentially weighted moving models using approximations.
Knowledge distillation introduced in the deep learning context is a method to transfer knowledge from one architecture to another. In particular, when the architectures are identical, this is called self-distillation. The idea is to feed in predictions of the trained model as new target values for retraining (and itera…
We study the problem of fitting an ultrametric distance to a dissimilarity graph in the context of hierarchical cluster analysis. Standard hierarchical clustering methods are specified procedurally, rather than in terms of the cost function to be optimized. We aim to overcome this limitation by presenting a general opt…
The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of new models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, …
EGL optimizes complex functions without fitting them, achieving state-of-the-art results.
GONs improve predictions of maximizers from noisy black-box functions.
Optimistic estimate predicts best fitting performance of nonlinear models.
In the spirit of the emergent field of econophysics, a goodness-of-fit test for the Power-Law distribution, based on the Empirical Distribution Function (EDF) is presented, and related problems are discussed. An analysis of the tail behaviour of the daily logarithmic variation of the Mexican Stock Market Index (IPC), s…
An innovative extension of Geometric Brownian Motion model is developed by incorporating a weighting factor and a stochastic function modelled as a mixture of power and trigonometric functions. Simulations based on this Modified Brownian Motion Model with optimal weighting factors selected by goodness of fit tests, sub…
The -generalised distribution fits daily stock returns well.
The optimal dividend problem by De Finetti (1957) has been recently generalized to the spectrally negative Lévy model where the implementation of optimal strategies draws upon the computation of scale functions and their derivatives. This paper proposes a phase-type fitting approximation of the optimal strategy. We con…
Fourier representation improves KSD for infinite-dimensional data.