arXiv research

New method for constructing truncated vine copulas.

Efficiently estimate Boolean product distribution parameters from truncated samples.

Paper proposes approximate Stein classes for efficient truncated density estimation.

Paper proposes a method to estimate truncated density models using Score Matching.

Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large e…

The method approximates stationary distributions of Markov models by truncating irrelevant states.

Typically, operational risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for operational risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modelled by the P…

The problem of an arbitrary truncated Levy flight description using the method of cumulant approach has been solved. The set of cumulants of the truncated Levy distribution given the assumption of arbitrary truncation has been found. The influence of truncation shape on the truncated Levy flight properties in the Gauss…

Estimates inverse temperature of Ising models with a single sample.

Choppy optimizes ranked list truncation using Transformer architecture.

In the paper "On Truncated Variation of Brownian Motion with Drift" (Bull. Pol. Acad. Sci. Math. 56 (2008), no.4, 267 - 281) we defined truncated variation of Brownian motion with drift, $W_t = B_t + μt, t\geq 0,$ where $(B_t)$ is a standard Brownian motion. Truncated variation differs from regular variation by neglect…

Soft Truncation improves diffusion model performance by balancing loss scales across diffusion times.

Optimal algorithm learns Gaussian under halfspace truncation with minimal samples.

Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncating backpropagation after a fixed number of lags. In practice, choosing the optimal truncation length is difficult: TBPTT will not converge …

Adaptive Nucleus Truncation Improves Long-Form Reasoning

We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The truncated variables are assumed latent and integrated out to induce a marginal m…

Estimates domain truncation error for option pricing PDEs.

We derive a novel variational expectation maximization approach based on truncated posterior distributions. Truncated distributions are proportional to exact posteriors within subsets of a discrete state space and equal zero otherwise. The treatment of the distributions' subsets as variational parameters distinguishes …

We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the applic…

The paper analyzes and mitigates biases in scalable Gaussian Process methods.

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribu…

Paper defines new risk measures for elliptical distributions.

In the context of the Dragulescu-Yakovenko (2000) model, we show that empirical income distribution with truncated datasets, cannot be properly modeled by the one-parameter exponential distribution. However, a truncated version characterized by an exponential distribution with two parameters gives an accurate fit.

Bayesian method estimates LTLL distribution parameters for time-to-event data.

The paper efficiently estimates parameters from truncated Gaussian and linear models.

Paper proposes robust estimators for heavy-tailed data with infinite variance.

Proposes a method to handle sparse multiway count data with false zeros using zero-truncated Poisson regression.

New DP framework using data truncation for efficient estimation.

In recent studies the truncated Levy process (TLP) has been shown to be very promising for the modeling of financial dynamics. In contrast to the Levy process, the TLP has finite moments and can account for both the previously observed excess kurtosis at short timescales, along with the slow convergence to Gaussian at …

Unified framework for mean testing under truncation bias.

Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian non-parametrics. While these models free from choosing the number of components a-priori, computationally attractive variational inference often reintroduces the need to do so, via a truncation on the variational distribution. In this paper we presen…

Score matching method improves density estimation for truncated data on manifolds.

The paper investigates the convergence of Vendi scores under finite samples and introduces a truncated version for better performance.

We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including sigmoid, tanh and ReLU, the most commonly used nonlinearities in neural…

UDN adapts depth to data complexity, outperforming standard neural networks.

Paper tackles overestimation bias in continuous control, improving performance by 25%.

Combines ML and DA to infer unresolved scale parametrisation from noisy data.

We consider an appoximation of a catenoid constructed from "odd" truncated cones that maintains minimality in a certain sense. Thorough this procedure, we obtain a discrete curve approximating a catenary by exploiting the fact that it is the function that generates a catenoid. In this investigation, the theory of the G…

New COS method formula improves option pricing accuracy.

We solve for functions from their truncated Hilbert transforms using Chebyshev series.

The generalized correlation approach, which has been successfully used in statistical radio physics to describe non-Gaussian random processes, is proposed to describe stochastic financial processes. The generalized correlation approach has been used to describe a non-Gaussian random walk with independent, identically d…

Lower bound shows super-polynomial gap for estimating truncated Gaussian means.

As in standard linear regression, in truncated linear regression, we are given access to observations $(A_i, y_i)_i$ whose dependent variable equals $y_i= A_i^{\rm T} \cdot x^* + η_i$, where $x^*$ is some fixed unknown vector of interest and $η_i$ is independent noise; except we are only given an observation if its dep…

New numerical method for non-linear asset price model with CEV volatility.

We show that generalised geometry gives a unified description of maximally supersymmetric consistent truncations of ten- and eleven-dimensional supergravity. In all cases the reduction manifold admits a "generalised parallelisation" with a frame algebra with constant coefficients. The consistent truncation then arises …

The paper studies deformation spaces of Coxeter truncation polytopes.

New kernels capture both local and non-local interactions efficiently.

The paper connects quantum $6j$-symbols to tetrahedra volumes via discrete Fourier transforms.