arXiv research

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

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 …

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

DUE framework models unknown equations from data using deep learning.

RTSCV detects unknown unknowns to improve model performance.

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 …

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…

Method learns model for unknown stochastic system from data.

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

FML uses neural networks to model unknown systems accurately.

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…

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

New method infers causal effects without knowing control variables.

Generative ODE model learns unknown variables in medical systems.

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

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…

Localization of unknown faults in industrial systems is a difficult task for data-driven diagnosis methods. The classification performance of many machine learning methods relies on the quality of training data. Unknown faults, for example faults not represented in training data, can be detected using, for example, ano…

Proposes a new framework for open set recognition using conditional probabilistic generative models.

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

We study the problem of learning an unknown mixture of $k$ rankings over $n$ elements, given access to noisy samples drawn from the unknown mixture. We consider a range of different noise models, including natural variants of the "heat kernel" noise framework and the Mallows model. For each of these noise models we giv…

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.

Paper tackles open set domain adaptation by detecting unknown classes.

In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but sometimes can be observed. This paper proposes a methodology to produce a hybrid mode…

DeepONet models system discrepancies with low data.

Algorithm minimizes regret while adhering to unknown safety constraints.

New method splits unknown covariance Gaussians into independent parts.

Despite significant effort in understanding complex systems (CS), we lack a theory for modeling, inference, analysis and efficient control of time-varying complex networks (TVCNs) in uncertain environments. From brain activity dynamics to microbiome, and even chromatin interactions within the genome architecture, many …

We consider partially observed multiscale diffusion models that are specified up to an unknown vector parameter. We establish for a very general class of test functions that the filter of the original model converges to a filter of reduced dimension. Then, this result is used to justify statistical estimation for the u…

We tackle tensor denoising with unknown permutations, achieving optimal recovery with polynomial estimators.

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

Enhances activity recognition in wearable computing with context awareness and uncertainty quantification.

A new estimator for evaluating policies in unknown environments.

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

New method identifies latent causal graphs without parametric assumptions.

Study robust learning without knowing perturbation sets, using interactions with attackers.

Paper models graph edge dependencies using latent variables for community detection.

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

New mixture models for clustering and density estimation of unknown distributions.

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 …

Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traff…

Two algorithms for nonlinear systems with unknown inputs are compared and implemented.

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

Develops inverse extended Kalman filter for predicting adversarial steps.

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…

Bayesian algorithm stabilizes unknown continuous-time systems from unstable data.

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

Optimal pricing strategy for unknown valuation models with noisy feedback.