arXiv research

NN-CUSUM detects changes in high-dimensional data using neural networks.

New CUSUM method detects changes in Hawkes networks efficiently.

Paper optimizes change detection in unnormalized distributions.

Efficient online kernel CUSUM detects changes quickly and accurately.

New CUSUM algorithm detects changes in unnormalized models.

FOCuS detects changes in mean from high-frequency data efficiently.

Paper detects and estimates breaks in high-dimensional functional time series.

Detecting weak clustered signal in spatial data is important but challenging in applications such as medical image and epidemiology. A more efficient detection algorithm can provide more precise early warning, and effectively reduce the decision risk and cost. To date, many methods have been developed to detect signals…

Fast detection of changepoints in linear regression models.

New exact tests detect changepoints in binary and count data, especially when normal approximations fail.

The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems und…

New method detects and locates changes in spatio-temporal point processes.

New method detects changes in high-dimensional Markov processes without explicit likelihood evaluation.

Deep learning generates efficient change-point detection methods.

Detects graph topology changes from noisy signals using prior spectral information.

Develops a nonparametric framework for detecting changes in sequential data.

We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned over a dataset that is supposed not to contain any anomaly. We then use the model o…

The hypothesis of randomness is fundamental in statistical machine learning and in many areas of nonparametric statistics; it says that the observations are assumed to be independent and coming from the same unknown probability distribution. This hypothesis is close, in certain respects, to the hypothesis of exchangeab…

We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we p…

New algorithms detect and react to multiple change points in online learning.

Boosts change-point detection power with optimal sub-sampling.

KCUSUM detects abrupt changes in real-time data streams efficiently.

New algorithm catches moving subspaces in bandit problems.

New method detects strong calibration in ML models, even for small poorly calibrated subgroups.

This paper improves change-point detection for complex data streams using denoising score matching.

Develops methods to test nonstationarity and detect change points in RL.

We study the problem of detecting a change in the mean of one-dimensional Gaussian process data. This problem is investigated in the setting of increasing domain (customarily employed in time series analysis) and in the setting of fixed domain (typically arising in spatial data analysis). We propose a detection method …

Optimizes quickest change detection with bounded means under ARL constraint.

The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bia…

The Lucas critique has exposed the problem of the trade-off between changes in monetary policy and structural breaks in economic time series. The search for and characterisation of such breaks has been a major econometric task ever since. We have developed an integral technique similar to CUSUM using an empirical model…

New monitoring method detects ML risk models' performance changes in medical interventions.

We study lightlike submanifolds of indefinite statistical manifolds. Contrary to the classical theory of submanifolds of statistical manifolds, lightlike submanifolds of indefinite statistical manifolds need not to be statistical submanifold. Therefore we obtain some conditions for a lightlike submanifold of indefinite…

Kenmotsu geometry is a valuable part of contact geometry with nice applications in other fields such as theoretical physics. In this article, we study the statistical counterpart of a Kenmotsu manifold, that is, Kenmotsu statistical manifold with some related examples. We investigate some statistical curvature properti…

Study anti-invariant submersions from holomorphic statistical manifolds.

Unified approach to private statistics from empirical to population data.

The paper introduces a statistical version of contact CR-product for Sasakian statistical manifolds.

The Bonnet theorem is proven for statistical manifolds.

Enhances power of covariance matrix tests for high-dimensional data.

This paper simplifies computing higher-order $U$-statistics efficiently.

This paper studies the geometry of immersions into statistical manifolds. A necessary and sufficient condition is obtained for statistical manifold structures to be dual to each other for a non-degenerate equiaffine immersion. Then we obtain conditions for realizing an n-dimensional statistical manifold in an (n+1)-dim…

This work uses statistical mechanics to explain AI learning.

The differential geometry of Kenmotsu manifold is a valuable part of contact geometry with nice applications in other fields such as theoretical physics. In fact, its statistical counterpart, that is, Kenmotsu statistical manifold also has same importance as that of Kenmotsu manifold. Theoretical physicists have also b…

Author presents the second variational formula for statistical biharmonic maps.

Study CR-statistical submanifolds in holomorphic statistical spaces.

New statistics are introduced that maintain the Fisher metric structure closely, akin to sufficient statistics.

Lightlike hypersurfaces of a statistical manifold are studied. It is shown that a lightlike hypersurface of a statistical manifold is not a statistical manifold with respect to the induced connections, but the screen distribution has a canonical statistical structure. Some relations between induced geometric objects wi…

Proves Gerber statistic is always non-negative.

This paper deals with the applications of an optimization method on submanifolds, that is, geometric inequalities can be considered as optimization problems. In this regard, we obtain optimal Casorati inequalities and Chen-Ricci inequality for a statistical submanifold in a statistical warped product manifold of type $…