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.
Incorporates matrix exponential into generative flows for improved performance.
New linear flows using exponential of linear transformations improve generative models.
The study explores generalized divergences and exponential families with a focus on sufficient conditions and laws of large numbers.
Paper introduces kernel deformed exponential families for sparse continuous attention.
Groups on CAT(0) cube complexes grow exponentially uniformly.
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…
New insights into natural exponential families improve regret bounds for bandit problems.
Introduces a new theoretical framework for exponential smoothing.
Maximum likelihood learning with exponential families leads to moment-matching of the sufficient statistics, a classic result. This can be generalized to conditional exponential families and/or when there are hidden data. This document gives a first-principles explanation of these generalized moment-matching conditions…
We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models [Yang et al. 2015] did not allow positive dependencies for the exponential and Poisson generalizat…
EFDA extends LDA to non-Gaussian models using exponential families.
Boosting with tempered exponential measures improves AdaBoost's convergence rate.
A new machine learning model uses matrix exponentials for universal approximation.
Exponential proportion of pseudo-Anosovs in mapping class groups.
Study symmetry groups and curves from sums of exponentials.
In this paper we introduce a new type of exponential map in semi-simple compact Lie groups, which is related to the sub-Riemannian geometry generated by the orthogonal complement of a Cartan subalgebra in a similar way to how the group exponential map is related to the Riemannian geometry.
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.
Normalization layers control deep neural network capacity, improving stability and generalization.
Exponentially smoothed RNNs improve industrial forecasting.
We study tilting subweibull distributions and their tail behavior.
This work achieves exponential concentration in heavy-tailed data over CAT(κ) spaces using the Fréchet median.
Develops a new exponential map for time-varying vector fields.
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 …
The study provides error bounds for the generalized Lasso with sub-exponential data.
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup. Several quantum algorithms for discr…
New model captures time-varying volatility with stochastic exponential tails.
Develops a lifting theory for exponential maps in semi-Riemannian geometry.
New Thompson sampling algorithm reduces regret for exponential family bandits.
Efficient method for learning continuous exponential families beyond Gaussian.
We study the exponential map of connected symmetric spaces and characterize, in terms of midpoints and of infinitesimal conditions, when it is a diffeomorphism, generalizing the Dixmier-Saito theorem for solvable Lie groups. We then give a geometric characterization of the (strongly) exponential solvable symmetric spac…
The goal of this paper is to prove a result conjectured in Föllmer and Schachermayer [FS07], even in slightly more general form. Suppose that S is a continuous semimartingale and satisfies a large deviations estimate; this is a particular growth condition on the mean-variance tradeoff process of S. We show that S then …
We provide equivalence of numerous no-free-lunch type conditions for financial markets where the asset prices are modeled as exponential Levy processes, under possible convex constraints in the use of investment strategies. The general message is the following: if any kind of free lunch exists in these models it has to…
Study shows exponential error reduction in multiclass classification without bias-variance trade-off.
This paper analyzes VAE approximation errors in conditional exponential families.
Paper proposes SLINK clustering for nonparametric data sequences with improved consistency.
New energy functional and fields for Yang-Mills theory, proving monotonicity and vanishing theorems.
We investigate a class of quadratic-exponential growth BSDEs with jumps. The quadratic structure introduced by Barrieu & El Karoui (2013) yields the universal bounds on the possible solutions. With local Lipschitz continuity and the so-called A_gamma-condition for the comparison principle to hold, we prove the existenc…
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 …
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…
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. Bu…
This paper extends exponential smoothing to distributional time series using Wasserstein distance.
The paper proposes a method to train time-varying generative models using natural gradients.
This paper studies stability of the exponential utility maximization when there are small variations on agent's utility function. Two settings are considered. First, in a general semimartingale model where random endowments are present, a sequence of utilities defined on R converges to the exponential utility. Under a …
Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and t…
Paper analyzes sparse aggregation in GLMs with Kullback-Leibler risk bounds.
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 introduces exponential-wrapped distributions on symmetric spaces for better data modeling.
Frame flows on certain symmetric spaces mix exponentially.