arXiv research

Incorporates matrix exponential into generative flows for improved performance.

New linear flows using exponential of linear transformations improve generative models.

Paper compares two hedging strategies for Lévy models.

Develops SQR models for multivariate exponential families allowing positive dependencies.

New model captures time-varying volatility with stochastic exponential tails.

We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs), directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks …

In a Markovian stochastic volatility model, we consider financial agents whose investment criteria are modelled by forward exponential performance processes. The problem of contingent claim indifference valuation is first addressed and a number of properties are proved and discussed. Special attention is given to the c…

ELNN uses neural networks for improved option pricing.

The paper studies variable annuity benefits using exponential functionals of Levy processes.

Quantum computing speeds up asset pricing models exponentially.

We propose a novel approach for density estimation with exponential families for the case when the true density may not fall within the chosen family. Our approach augments the sufficient statistics with features designed to accumulate probability mass in the neighborhood of the observed points, resulting in a non-para…

A two-network architecture learns intractable exponential family models.

Motivated by the pricing of lookback options in exponential Lévy models, we study the difference between the continuous and discrete supremum of Lévy processes. In particular, we extend the results of Broadie et al. (1999) to jump-diffusion models. We also derive bounds for general exponential Lévy models.

Quantum algorithm improves generative models exponentially.

Exponentially smoothed RNNs improve industrial forecasting.

There has been an explosion of interest in statistical models for analyzing network data, and considerable interest in the class of exponential random graph (ERG) models, especially in connection with difficulties in computing maximum likelihood estimates. The issues associated with these difficulties relate to the bro…

Efficient method for learning continuous exponential families beyond Gaussian.

Develops a semi-supervised learning method using exponential tilt mixture models.

Exponential distribution is ubiquitous in the framework of multi-agent systems. An alternative approach with an economic motivation to derive the exponential distribution in the framework of iterations in the space of distributions is disclosed.

We develop a new Monte Carlo variance reduction method to estimate the expectation of two commonly encountered path-dependent functionals: first-passage times and occupation times of sets. The method is based on a recursive approximation of the first-passage time probability and expected occupation time of sets of a Le…

The paper calculates prices for special options using mixed-exponential jumps.

Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly…

Proposes a new model using exponential smoothing cells for robust time series analysis.

The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions, such as when there is some sparsity pattern of the optimal parameter. This work characterizes a certain strong conve…

The paper introduces a new method for graph embedding using exponential family distributions.

GRM models k-way dependencies in univariate exponential families.

Auto-regressive models improve smoothing efficiency with exponentially tapered windows.

EFDA extends LDA to non-Gaussian models using exponential families.

SMRL uses score matching for efficient RL with exponential family models.

A new method for exponentially weighted moving models using approximations.

In the setting of exponential investors and uncertainty governed by Brownian motions we first prove the existence of an incomplete equilibrium for a general class of models. We then introduce a tractable class of exponential-quadratic models and prove that the corresponding incomplete equilibrium is characterized by a …

Improved Gibbs sampler speeds up Bayesian exponential smoothing model.

CDEFs reduce model complexity and uncover time correlations.

Improves variational inference for sparse models using mixtures of exponential families.

A new machine learning model uses matrix exponentials for universal approximation.

Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the Bernoulli case. Here we extend them by proving asymptotic optimality of the algorithm using the Jeffreys prior for 1-dimensional exponential …

Chentsov's theorem proved for exponential families.

The accuracy of option pricing models using exponential Lévy processes is investigated.

The paper explores risk-minimization for exponential additive models, providing mathematical expressions and numerical examples.

Efficiently computes option pricing matrix exponentials.

The paper introduces exponential-wrapped distributions on symmetric spaces for better data modeling.

Quantum networks offer exponential communication savings for large machine learning models.

We study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. While at shorter microscopic time lags the distribution has power-law tails, for mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an expon…

Study examines optimal hedging of barrier options in leptokurtic equity returns.

Exponential family extensions of principal component analysis (EPCA) have received a considerable amount of attention in recent years, demonstrating the growing need for basic modeling tools that do not assume the squared loss or Gaussian distribution. We extend the EPCA model toolbox by presenting the first exponentia…

The paper studies scaling limits of hedging prices in financial models.

Paper establishes universal lower bounds and optimal rates for clustering sub-exponential mixture models.

The study shows that the visible range from a point on harmonic manifolds follows an exponential distribution.