arXiv research

Algorithm estimates Gaussian parameters under unknown truncation sets.

New algorithm for truncated linear regression without knowing the survival set.

New algorithms estimate parameters of Gaussian and non-Gaussian distributions from truncated samples.

Optimal algorithm learns Gaussian under halfspace truncation with minimal samples.

This paper presents a new algorithm, termed \emph{truncated amplitude flow} (TAF), to recover an unknown vector $\bm{x}$ from a system of quadratic equations of the form $y_i=|\langle\bm{a}_i,\bm{x}\rangle|^2$, where $\bm{a}_i$'s are given random measurement vectors. This problem is known to be \emph{NP-hard} in genera…

Unified framework for mean testing under truncation bias.

Blind deconvolution is a ubiquitous problem of recovering two unknown signals from their convolution. Unfortunately, this is an ill-posed problem in general. This paper focuses on the {\em short and sparse} blind deconvolution problem, where the one unknown signal is short and the other one is sparsely and randomly sup…

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

A new method for multi-objective Bayesian optimization using entropy search and variational lower bound maximization.

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…

We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a $d$-variate normal ${\cal N}(\mathbfμ,\mathbfΣ)$ means a samples is only re…

In structured output prediction tasks, labeling ground-truth training output is often expensive. However, for many tasks, even when the true output is unknown, we can evaluate predictions using a scalar reward function, which may be easily assembled from human knowledge or non-differentiable pipelines. But searching th…

The paper presents a method to infer unknown forcing functions in differential equations using Gaussian processes and adjoints.

Paper develops fast low-rank approximation for smoothing splines.

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

Unified method for calculating financial option prices from characteristic functions.

Optimality of TS with noninformative priors proven for Pareto model.

We show how to compute lower bounds for the supremum Bayes error if the class-conditional distributions must satisfy moment constraints, where the supremum is with respect to the unknown class-conditional distributions. Our approach makes use of Curto and Fialkow's solutions for the truncated moment problem. The lower …

Smoothed analysis shows that many classes become learnable from positive-only samples.

Paper extends LME models to allow sign constraints on coefficients with SDTN random effects.

The paper estimates common mean of entangled Gaussians with bounded variances.

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…

A novel approach termed \emph{stochastic truncated amplitude flow} (STAF) is developed to reconstruct an unknown $n$-dimensional real-/complex-valued signal $\bm{x}$ from $m$ `phaseless' quadratic equations of the form $ψ_i=|\langle\bm{a}_i,\bm{x}\rangle|$. This problem, also known as phase retrieval from magnitude-onl…

Truncated CauchyNMF robustly learns subspaces from noisy data.

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…

New method for constructing truncated vine copulas.

Paper presents a method to reduce prediction variance of DNNs for unknown systems.

Paper proposes approximate Stein classes for efficient truncated density estimation.

Paper defines new risk measures for elliptical distributions.

The paper examines optimal insurance design using Lambda-Value-at-Risk.

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

New DP framework using data truncation for efficient estimation.

Approach for recovering shared structure from multiple networks with unknown noise.

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 …

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

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

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.

Paper proposes using truncated normal distribution for RRC model, improving detection of minority classes.

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…

Spectrally-truncated KRR outperforms full KRR for large data.

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

Paper tackles moment estimation under covariate shift with a two-stage algorithm.