arXiv research

New DP framework using data truncation for efficient estimation.

Efficiently estimate Boolean product distribution parameters from truncated samples.

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

Study identifies and analyzes three types of errors in learning Fourier operators.

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

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…

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 study the problem of estimating the parameters of a Gaussian distribution when samples are only shown if they fall in some (unknown) subset $S \subseteq \R^d$. This core problem in truncated statistics has long history going back to Galton, Lee, Pearson and Fisher. Recent work by Daskalakis et al. (FOCS'18), provide…

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…

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…

Develops a new trading strategy for statistical arbitrage with path-dependent signals.

Optimized Franz-Parisi criterion matches SQ lower bounds for various statistical models.

Decoding strategies often exclude human-like tokens, creating a detectable gap in generated text.

We consider large scale empirical risk minimization (ERM) problems, where both the problem dimension and variable size is large. In these cases, most second order methods are infeasible due to the high cost in both computing the Hessian over all samples and computing its inverse in high dimensions. In this paper, we pr…

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

Estimates inverse temperature of Ising models with a single sample.

We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family …

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

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

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…

Paper develops IFTRR to solve sparse generalized eigenvalue problems efficiently.

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

Survey of extreme value modeling techniques for insurance.

Paper develops approximation and statistical theory for signature-based path regression.

Sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large family of high-dimensional statistical models, including sparse Fisher's discriminant analysis, canonical correlation analysis, and sufficient dimension reduction. Sparse GEP involves solving a non-convex optimization problem. Most existing met…

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…

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

We describe financial systems as condensates, similar to Bose-Einstein condensates, and calculate statistical distributions following from the model. The calculated distributions of investments into speculated financial assets are found equivalent to a Pareto distribution, and the calculated distributions of the price …

This article reviews and explains HMC-based methods for sampling constrained continuous distributions.

Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities, due to the Gaussian assumption. In this paper, we introduce a novel va…

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 improves sampling from logconcave distributions truncated on polytopes.

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…

CPCR mitigates bias in PCR for overparameterized models.

Paper proposes approximate Stein classes for efficient truncated density estimation.

Paper defines new risk measures for elliptical distributions.

TSNPE improves SBI efficiency and scalability.

Motivated by a recent result of Daskalakis et al. 2018, we analyze the population version of Expectation-Maximization (EM) algorithm for the case of \textit{truncated} mixtures of two Gaussians. Truncated samples from a $d$-dimensional mixture of two Gaussians $\frac{1}{2} \mathcal{N}(\vecμ, \vecΣ)+ \frac{1}{2} \mathca…

Study analyzes fluctuations in Mexican financial market index.

Efficiently estimates covariance for sub-Weibull vectors with sub-Gaussian rate.

New statistical models for predicting ranked preferences from partial orders.

Several statistical models are given in the form of unnormalized densities, and calculation of the normalization constant is intractable. We propose estimation methods for such unnormalized models with missing data. The key concept is to combine imputation techniques with estimators for unnormalized models including no…

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

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 …