arXiv research

Paper proposes a probabilistic method to handle missing data in decision trees.

New method improves prediction accuracy in business process mining by handling concept drift.

Trinary decision tree improves handling of missing data in machine learning.

Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.

Paper reviews and compares methods for handling imbalanced data.

PyTorch Frame simplifies multi-modal tabular learning with modular data and model handling.

VAEM extends VAEs to handle mixed-type data heterogeneity.

Pairwise similarities and dissimilarities between data points might be easier to obtain than fully labeled data in real-world classification problems, e.g., in privacy-aware situations. To handle such pairwise information, an empirical risk minimization approach has been proposed, giving an unbiased estimator of the cl…

Study handles in 4-manifolds with cyclic fundamental group.

Elliptic surfaces without 1-handles proven for specific cases.

A new approach switches between simple and complex models to handle concept drifts in regression tasks.

The paper develops methods to handle missing data using regularized M-estimation in reproducing kernel Hilbert space.

Study of 3-handle attachments in 4D manifolds using Kirby calculus.

Two new methods improve clustering with missing data.

We propose a novel kernel based post selection inference (PSI) algorithm, which can not only handle non-linearity in data but also structured output such as multi-dimensional and multi-label outputs. Specifically, we develop a PSI algorithm for independence measures, and propose the Hilbert-Schmidt Independence Criteri…

The study shows conditions for elliptic surfaces without 1-handles.

In this paper, a scale mixture of Normal distributions model is developed for classification and clustering of data having outliers and missing values. The classification method, based on a mixture model, focuses on the introduction of latent variables that gives us the possibility to handle sensitivity of model to out…

Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all t…

New deep probabilistic model handles missing data in time series forecasting.

This paper tackles imbalanced data in binary classification problems.

The main contribution of this paper is the development of a new decision tree algorithm. The proposed approach allows users to guide the algorithm through the data partitioning process. We believe this feature has many applications but in this paper we demonstrate how to utilize this algorithm to analyse data sets cont…

Finding an optimal parameter of a black-box function is important for searching stable material structures and finding optimal neural network structures, and Bayesian optimization algorithms are widely used for the purpose. However, most of existing Bayesian optimization algorithms can only handle vector data and canno…

Random forests are a statistical learning method widely used in many areas of scientific research because of its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forest approaches are not flexible enough to handle he…

New algorithms handle missing data to improve fairness in machine learning.

Combines pseudo-point and state space approximations for scalable GPs.

Harer-Kas-Kirby conjectured that every handle decomposition of the elliptic surface E(1)_{2,3} requires both 1- and 3-handles. We prove that the elliptic surface E(n)_{p,q} has a handle decomposition without 1-handles for $n\geq 1$ and (p,q)=(2,3),(2,5),(3,4),(4,5).

Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training data. This paper presents a unifying framework for supervised classification with general ensembles of training dat…

We review Giroux's contact handles and contact handle attachments in dimension three and show that a bypass attachment consists of a pair of contact 1 and 2-handles. As an application we describe explicit contact handle decompositions of infinitely many pairwise non-isotopic overtwisted 3-spheres. We also give an alter…

D.Nash defined a family of homotopy 4-spheres in [11]. Proving that his manifolds ${\mathcal S}_{m,n,m',n'}$ are all real $S^4$, we find that they have handle decomposition with no 1-handles, two 2-handles and two 3-handles. The handle structures give new potential counterexamples of Property 2R conjecture.

Develops new algorithms for QRF to handle mixed-frequency and longitudinal data.

Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomp…

Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…

New findings show infinitely many knots cannot be smoothly round handle slices.

Paper introduces methods to handle missing data in probabilistic regression trees.

Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because s…

New methods handle both data and network heterogeneity in federated learning.

Kernel ridge regression imputation with consistent variance estimation for handling missing data.

A 2-dimensional braid over an oriented surface-knot $F$ is presented by a graph called a chart on a surface diagram of $F$. We consider 2-dimensional braids obtained by an addition of 1-handles equipped with chart loops. We introduce moves of 1-handles with chart loops, called 1-handle moves, and we investigate how muc…

Homology handles with trivial Alexander polynomial bound a 3D sphere.

The paper constructs contractible manifolds with knotted spheres.

We use the conformal method to obtain solutions of the Einstein-scalar field gravitational constraint equations. Handling scalar fields is a bit more challenging than handling matter fields such as fluids, Maxwell fields or Yang-Mills fields, because the scalar field introduces three extra terms into the Lichnerowicz e…

Defines extended TQFTs using handle attachments.

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, …

In many real-world applications, data are often collected in the form of stream, and thus the distribution usually changes in nature, which is referred as concept drift in literature. We propose a novel and effective approach to handle concept drift via model reuse, leveraging previous knowledge by reusing models. Each…

A new RBM model handles both linear and log-amplitude spectrograms.

Two methods use BART to model missing data in leaf photosynthetic trait data.

Machine learning portfolios perform well with simple imputation of missing data.

Study handles in sutured manifolds and knots, finding varied handle numbers and unique surfaces.