arXiv research

Efficiently estimate Boolean product distribution parameters from truncated samples.

Optimal algorithm learns Gaussian under halfspace truncation with minimal samples.

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

Adaptive Nucleus Truncation Improves Long-Form Reasoning

In this paper, we develop a general theory of truncated inverse binomial sampling. In this theory, the fixed-size sampling and inverse binomial sampling are accommodated as special cases. In particular, the classical Chernoff-Hoeffding bound is an immediate consequence of the theory. Moreover, we propose a rigorous and…

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

Unified framework for mean testing under truncation bias.

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

Estimates inverse temperature of Ising models with a single sample.

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…

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…

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…

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

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

Paper proposes approximate Stein classes for efficient truncated density estimation.

New DP framework using data truncation for efficient estimation.

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

New methods stabilize Q-learning with linear approximations.

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…

New method improves sampling from logconcave distributions truncated on polytopes.

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…

Kernel ridge regression (KRR) is a well-known and popular nonparametric regression approach with many desirable properties, including minimax rate-optimality in estimating functions that belong to common reproducing kernel Hilbert spaces (RKHS). The approach, however, is computationally intensive for large data sets, d…

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

Adaptive truncation improves privacy in online Bayesian estimation.

Paper estimates spectral risk measures for insurance data with truncated and censored data.

Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent manner when initialized properly. However, the performance of existing algorithms is highly sensitive in the presence of outliers that may …

We propose a new framework for Hamiltonian Monte Carlo (HMC) on truncated probability distributions with smooth underlying density functions. Traditional HMC requires computing the gradient of potential function associated with the target distribution, and therefore does not perform its full power on truncated distribu…

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…

SeqRF straightens generative model flows to speed up sampling.

A new method reduces variance in PG methods for RL, improving efficiency and convergence.

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…

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

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

Fractured Sampling improves LLM reasoning efficiency by truncating CoT trajectories.

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

TKRR improves KRR performance by aligning target functions with kernels.

The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of pr…

Computing partition functions, the normalizing constants of probability distributions, is often hard. Variants of importance sampling give unbiased estimates of a normalizer Z, however, unbiased estimates of the reciprocal 1/Z are harder to obtain. Unbiased estimates of 1/Z allow Markov chain Monte Carlo sampling of "d…

Improved neural likelihood estimation for SSMs with truncated-SNL.

This paper addresses challenges in flexibly modeling multimodal data that lie on constrained spaces. Such data are commonly found in spatial applications, such as climatology and criminology, where measurements are restricted to a geographical area. Other settings include domains where unsuitable recordings are discard…

New algorithm improves inference for flexible models with infinite latent features.

Optimality of TS with noninformative priors proven for Pareto model.

New method uses SDEs for accurate non-uniformly sampled time series analysis.

New method reduces sample complexity for robust reinforcement learning.

Paper proposes robust methods for estimating optimal treatment rules with censored survival data.

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 method estimates treatment effects over time for survival data, improving accuracy and smoothness.

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…