arXiv research

AI agent learns to handle unknown unknown states in reinforcement learning.

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

Identification of patterns from discrete data time-series for statistical inference, threat detection, social opinion dynamics, brain activity prediction has received recent momentum. In addition to the huge data size, the associated challenges are, for example, (i) missing data to construct a closed time-varying compl…

Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data. In many real-world applications, however, some potential …

We develop an unsupervised, nonparametric, and scalable statistical learning method for detection of unknown objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes and sizes in the presence of nonparametri…

In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to known labels. This paper studies a new problem setting in which there are unknown classes in the training data misperceived as other labels, and thus …

DUE framework models unknown equations from data using deep learning.

Paper develops PAC-Bayes bounds for unknown linear systems.

Algorithm minimizes regret while adhering to unknown safety constraints.

RTSCV detects unknown unknowns to improve model performance.

Method learns model for unknown stochastic system from data.

New method infers causal effects without knowing control variables.

Safety filter for unknown discrete-time systems with learned models and noise covariance.

This work extends Ledoit-Wolf shrinkage to unknown mean covariance estimation.

Constructs non-asymptotic confidence regions for unknown functions in RKHS.

The article presents a method that improves the quality of classification of objects described by a combination of known and unknown features. The method is based on modernized Informational Neurobayesian Approach with consideration of unknown features. The proposed method was developed and trained on 1500 text queries…

Optimal algorithm for contextual bandits with unknown context distributions.

FML uses neural networks to model unknown systems accurately.

New algorithm recovers matrices with unknown correspondences.

Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets. In these scenarios we may need to rely on a human oracle to evaluate individual predictions; presenting the challenge to create query algorithms to guide the search for points that provide the most informa…

New algorithm detects changes in Markov kernels with unknown post-change kernel.

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

New method identifies latent causal graphs without parametric assumptions.

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

A new GP framework for discovering unknown functions and hypergraph structure.

Efficient algorithm for unknown linear systems with convex costs.

Paper tackles unknown variances in best-arm identification.

Paper tackles open set domain adaptation by detecting unknown classes.

BaCaDI discovers causal structures from unknown interventions.

Paper introduces method to estimate animal motion on unknown submanifolds using Koopman operator.

Generative ODE model learns unknown variables in medical systems.

Markowitz' celebrated optimal portfolio theory generally fails to deliver out-of-sample diversification. In this note, we propose a new portfolio construction strategy based on symmetry arguments only, leading to "Eigenrisk Parity" portfolios that achieve equal realized risk on all the principal components of the covar…

Study online learning in unknown Markov games with sublinear regret.

New algorithm achieves data-dependent regret bounds in MDPs with unknown transitions.

New algorithm reduces online learning error for unknown feature distributions.

This paper introduces the first asymptotically optimal strategy for a multi armed bandit (MAB) model under side constraints. The side constraints model situations in which bandit activations are limited by the availability of certain resources that are replenished at a constant rate. The main result involves the deriva…

OpenViewer tackles multi-view learning challenges with interpretability and generalization.

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

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

New method splits unknown covariance Gaussians into independent parts.

Consider a noisy linear observation model with an unknown permutation, based on observing $y = Π^* A x^* + w$, where $x^* \in \mathbb{R}^d$ is an unknown vector, $Π^*$ is an unknown $n \times n$ permutation matrix, and $w \in \mathbb{R}^n$ is additive Gaussian noise. We analyze the problem of permutation recovery in a …

MetaCaDI learns causal graphs and unknown interventions from few data instances.

GP-MRO discovers robust mixed strategies for unknown objectives.

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

GACBO optimizes unknown causal graphs with interventions.

We study statistical detection of grayscale objects in noisy images. The object of interest is of unknown shape and has an unknown intensity, that can be varying over the object and can be negative. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required…

Proposes a new model for high-dimensional data analysis with unknown link function.

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an ex…