arXiv research

Proposes a learned Bayesian Cramér-Rao bound for unknown measurement models.

Proposes using equivariant generative models for compressed sensing with unknown orientations.

New method models unknown systems with hidden parameters using neural networks.

New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.

Study on recovering supports of multiple sparse vectors from mixed linear measurements.

The problem of detecting data anomaly is considered. Under the null hypothesis that models anomaly-free data, measurements are assumed to be from an unknown distribution with some authenticated historical samples. Under the composite alternative hypothesis, measurements are from an unknown distribution positive distanc…

Algorithm minimizes regret while adhering to unknown safety constraints.

When eliciting judgements from humans for an unknown quantity, one often has the choice of making direct-scoring (cardinal) or comparative (ordinal) measurements. In this paper we study the relative merits of either choice, providing empirical and theoretical guidelines for the selection of a measurement scheme. We pro…

Generative models solve medical imaging inverse problems without needing paired data.

We introduce a measure to quantify ambiguity in deep learning models, improving their reliability.

Generative ODE model learns unknown variables in medical systems.

Paper tackles measure estimation in barycentric coding model.

Improved PINNs for solving PDEs with unknown measurement noise.

New method uses PINNs to solve complex PDEs with sparse measurements.

Quantum machine learning has received significant attention in recent years, and promising progress has been made in the development of quantum algorithms to speed up traditional machine learning tasks. In this work, however, we focus on investigating the information-theoretic upper bounds of sample complexity - how ma…

The paper develops adaptive confidence intervals for Efron's Gaussian two-groups model with unknown contamination.

New algorithm reduces prediction error in online learning without knowing base measure.

Paper proposes method for optimal control of unknown systems with latent states.

The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.

DUE framework models unknown equations from data using deep learning.

We evaluate the uncertainty quality in neural networks using anomaly detection. We extract uncertainty measures (e.g. entropy) from the predictions of candidate models, use those measures as features for an anomaly detector, and gauge how well the detector differentiates known from unknown classes. We assign higher unc…

Researchers reconstruct simple Riemannian manifolds from boundary wave arrival times.

We consider the robust phase retrieval problem of recovering the unknown signal from the magnitude-only measurements, where the measurements can be contaminated by both sparse arbitrary corruption and bounded random noise. We propose a new nonconvex algorithm for robust phase retrieval, namely Robust Wirtinger Flow to …

Bayesian nonparametric models improve tracking in cluttered environments.

The paper assesses quality measures for machine learning models using cross-validation.

Bayesian method synthesizes barrier certificates for unknown systems with latent states.

New method identifies latent causal graphs without parametric assumptions.

Paper infers intrinsic dimension from quasi-convex measurements.

We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentag…

Safe learning in uncertain systems with state measurements and optimization.

MAntRA combines machine learning and Bayesian methods for time-dependent reliability analysis of unknown systems.

This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogate of the unknown function in the form of a random Fourier expansion (RFE). The surrogate is updated whenever a new measurement is available,…

Paper introduces a Bayesian nonparametric approach for tracking multiple objects with spawning events.

PGD algorithms solve nonlinear inverse problems with generative priors using noisy measurements.

Bayesian inference identifies model parameters from financial data to detect arbitrage opportunities.

The theory of Compressed Sensing (CS) asserts that an unknown signal $x\in\mathbb{R}^p$ can be accurately recovered from an underdetermined set of $n$ linear measurements with $n\ll p$, provided that $x$ is sufficiently sparse. However, in applications, the degree of sparsity $\|x\|_0$ is typically unknown, and the pro…

Extends angular synchronization to heterogeneous groups, improving accuracy in multiple applications.

Due to their heterogeneity, insurance risks can be properly described as a mixture of different fixed models, where the weights assigned to each model may be estimated empirically from a sample of available data. If a risk measure is evaluated on the estimated mixture instead of the (unknown) true one, then it is impor…

A method for classifying points with minimal queries using Hermite polynomials.

Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of meas…

Estimates system parameters from a single observation using kernel-based score.

In "Unlabeled Sensing", one observes a set of linear measurements of an underlying signal with incomplete or missing information about their ordering, which can be modeled in terms of an unknown permutation. Previous work on the case of a single noisy measurement vector has exposed two main challenges: 1) a high requir…

New method infers unknown parameters in quantum sensing with high probability.

Paper estimates GMMs with unknown covariances using sparse regularization.

This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.

Algorithm selects optimal experiments in Markov chains to learn unknown quantities.

The problem of estimating an unknown discrete distribution from its samples is a fundamental tenet of statistical learning. Over the past decade, it attracted significant research effort and has been solved for a variety of divergence measures. Surprisingly, an equally important problem, estimating an unknown Markov ch…

In the theory of compressed sensing (CS), the sparsity ||x||_0 of the unknown signal x\in\R^p is commonly assumed to be a known parameter. However, it is typically unknown in practice. Due to the fact that many aspects of CS depend on knowing ||x||_0, it is important to estimate this parameter in a data-driven way. A s…