arXiv research

A new clustering model for mixed datasets combines continuous and non-continuous data.

Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, …

Paper improves generalization bounds for multi-kernel learning with mixed datasets.

A new distance for mixed-variable, hierarchical datasets with meta variables.

A new method clusters qualitative data with mixed variables, improving interpretability.

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data is challenging becau…

The paper proposes an efficient method to scale Bayesian inference for mixed multinomial logit models to very large datasets.

New method for conformal prediction under Markovian data reduces coverage gap.

Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operat…

New method for mixed memberships using symmetrized Laplacian inverse matrix.

We develop methodology for visualization of labeled mixed-featured datasets. We first investigate datasets with continuous features where our Max-Ratio Projection (MRP) method utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. O…

PAIN network improves imputation for mixed datasets.

Learning color mixing is difficult for novice painters. In order to support novice painters in learning color mixing, we propose a prediction model for semitransparent pigment mixtures and use its prediction results to create a Smart Palette system. Such a system is constructed by first building a watercolor dataset wi…

MC-GMENN improves neural networks for clustered data using Monte Carlo methods.

Paper tackles offline RL from mixed datasets with adaptive KL regularizer.

Mix-IRLS solves imbalanced mixed linear regression problems efficiently.

Bayesian test assesses dependence between mixed data types.

This paper compares methods for handling mixed-attribute data in GFMM neural networks.

Superpixel-mix enhances reliability in semantic segmentation.

Study compares clustering methods for mixed-type data.

metabeta uses neural networks to speed up Bayesian mixed-effects regression.

FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.

Method improves deep learning models for datasets with mixed approximate symmetries.

DPERC efficiently estimates covariance matrices for mixed data with missing values.

New online imputation method for mixed data improves accuracy and speed.

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attribute…

ARED introduces a new dataset for Argentina's real estate market.

A new data augmentation method selects mixed classes based on class distances for better performance.

Library learns Bayesian networks from mixed data without discretization.

Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigabytes in large-scale settings. To help manage this outsized memory consumption, we explore mixed dimension embeddings, an embedding layer arc…

GBMixed boosts mixed models for clustered data, estimating mean and variance flexibly.

Overfitting & underfitting and stable training are an important challenges in machine learning. Current approaches for these issues are mixup, SamplePairing and BC learning. In our work, we state the hypothesis that mixing many images together can be more effective than just two. Batchboost pipeline has three stages: (…

Mixed integer programming identifies critical neurons in neural networks.

DP-CDA generates synthetic data to enhance privacy in high-dimensional datasets.

New method for Bayesian inference on large datasets.

MPTE uses Transformer attention to estimate mixed-frequency factor models.

In this paper we provide a principled approach to solve a transductive classification problem involving a similar graph (edges tend to connect nodes with same labels) and a dissimilar graph (edges tend to connect nodes with opposing labels). Most of the existing methods, e.g., Information Regularization (IR), Weighted …

A new method for statistical inference using SGD under $φ$-mixing data.

SelectMix improves deep learning robustness against noisy labels.

GP-MRO discovers robust mixed strategies for unknown objectives.

Study compares neural and statistical models for Parkinson's disease progression from voice data.

The study introduces a holdout-based framework to assess synthetic data fidelity and privacy.

We focus on the problem of unsupervised cell outlier detection and repair in mixed-type tabular data. Traditional methods are concerned only with detecting which rows in the dataset are outliers. However, identifying which cells are corrupted in a specific row is an important problem in practice, and the very first ste…

The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodologies to effectively handle mixed data by making the data less heterogeneous with minimum loss of information. The other challenge is that …

A scalable model for high-dimensional longitudinal data.

A new model estimates mixed memberships for categorical data with weighted responses.

New model handles complex output dependence in large datasets.

i-Mix improves contrastive learning across domains without domain-specific augmentations.