arXiv research

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

A new algorithm speeds up elliptical slice sampling for truncated multivariate normals.

Tree++ graph kernel captures similarities at multiple granularities.

Tr-LinUCB reduces regret in stochastic linear bandits by truncating exploration.

TKRR improves KRR performance by aligning target functions with kernels.

COS method convergence conditions expanded for heavy-tailed distributions.

We study a stochastic multiplicative system composed of finite asynchronous elements to describe the wealth evolution in financial markets. We find that the wealth fluctuations or returns of this system can be described by a walk with correlated step sizes obeying truncated Levy-like distribution, and the cross-correla…

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…

BMRS offers a Bayesian approach to structured pruning of neural networks.

Efficiently estimate Boolean product distribution parameters from truncated samples.

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…

RANDomized-exploration policy Optimization via Multiple Importance Sampling with Truncation (RANDOMIST) for PO with mediator feedback.

Optimal algorithm learns Gaussian under halfspace truncation with minimal samples.

New method for constructing truncated vine copulas.

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…

Paper proposes approximate Stein classes for efficient truncated density estimation.

Paper defines new risk measures for elliptical distributions.

Neural networks solve SPDEs using Wiener chaos expansion.

New DP framework using data truncation for efficient estimation.

Unified framework for mean testing under truncation bias.

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

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 …

Truncated densities are probability density functions defined on truncated domains. They share the same parametric form with their non-truncated counterparts up to a normalizing constant. Since the computation of their normalizing constants is usually infeasible, Maximum Likelihood Estimation cannot be easily applied t…

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

New formula and properties of inverted Habiro series derived from GM series.

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…

Estimates domain truncation error for option pricing PDEs.

Choppy optimizes ranked list truncation using Transformer architecture.

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…

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

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 …

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

Adaptive Nucleus Truncation Improves Long-Form Reasoning

Estimates inverse temperature of Ising models with a single sample.

The paper studies deformation spaces of Coxeter truncation polytopes.

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

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…

CPCR mitigates bias in PCR for overparameterized models.

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

Learning with a {\it convex loss} function has been a dominating paradigm for many years. It remains an interesting question how non-convex loss functions help improve the generalization of learning with broad applicability. In this paper, we study a family of objective functions formed by truncating traditional loss f…

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

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…

Truncated SGD with heavy-tailed noise eliminates sharp local minima.

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 …