arXiv research

Paper tackles nonparametric classification with privacy constraints, achieving optimal accuracy.

Study builds a classifier for diffusions with unknown diffusion but known drifts.

Proposes a robust method for counterfactual classification.

Paper improves matrix-valued data classification using nonparametric LDA.

New method uses public data to achieve optimal nonparametric classification with privacy constraints.

Paper develops a privacy-preserving nonparametric regression method.

Proposes a deep learning method for effective data representation.

Survey of Bayesian nonparametric space partition models and their applications.

Undersampling often outperforms other methods in nonparametric classification.

InClass nets use neural networks to estimate CIMMs without assuming fixed parameters.

Novel nonparametric method for GLMs improves prediction and inference performance.

Sparse-penalized deep neural networks improve performance in weakly dependent processes.

Skew Gaussian Processes improve classification performance by allowing asymmetry.

Proposes a method for selecting important variables in high-dimensional data.

A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper introduces a new abnormality measure for decision making. The proposed method is evaluated on both synthetic and real data. The comparison w…

Active learning can't improve over passive in certain settings.

Metalearned neural circuit performs inference over open classes.

A new procedure, called DDa-procedure, is developed to solve the problem of classifying d-dimensional objects into q >= 2 classes. The procedure is completely nonparametric; it uses q-dimensional depth plots and a very efficient algorithm for discrimination analysis in the depth space [0,1]^q. Specifically, the depth i…

Paper tackles label noise in large datasets, purifying noisy data with a nonparametric framework.

Develops abstention procedure for nonparametric regression via variance testing.

An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described. A hierarchical Bayesian approach with a randomly scaled Gaussian prior is considered. The prior uses the graph Laplacian to take into account the underlying geometry of the graph. A method based on a…

This article describes an implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs. We consider a hierarchical Bayesian approach with a prior that is constructed by truncating a series expansion of the soft label function using the graph Laplacian eigenfunctions as basis f…

New framework for distributed nonparametric estimation under slow communication.

This paper investigates the problem of active learning for binary label prediction on a graph. We introduce a simple and label-efficient algorithm called S2 for this task. At each step, S2 selects the vertex to be labeled based on the structure of the graph and all previously gathered labels. Specifically, S2 queries f…

Stream mining poses unique challenges to machine learning: predictive models are required to be scalable, incrementally trainable, must remain bounded in size (even when the data stream is arbitrarily long), and be nonparametric in order to achieve high accuracy even in complex and dynamic environments. Moreover, the l…

DNCF framework recovers real scenes from imperfect images robustly.

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taki…

Unified Skew-Gaussian process framework for various regression and classification tasks.

New algorithms achieve near-optimal cumulative loss in nonparametric online learning and games.

Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.

A new $k$-NN algorithm using surprisal for robust and interpretable nonparametric learning.

In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data server is assumed to provide local neural network weights, which are modeled th…

Human learners have the natural ability to use knowledge gained in one setting for learning in a different but related setting. This ability to transfer knowledge from one task to another is essential for effective learning. In this paper, we study transfer learning in the context of nonparametric classification based …

Develops an online nonparametric classifier for massive data.

A new neural subsampling method reduces data volume for deep models.

We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional distributions. Under these assumptions, we establish an $Ω(K\log K)$ labeled samp…

The paper introduces a method to quantify uncertainty in neural networks without parametric assumptions.

Proposes LRR and LRLR for improving stock prediction accuracy.

Study on CNNs' learning rates and approximation capacities.

Bayesian model improves classification performance with flexible uncertainty modeling.

Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an uns…

Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest. Here we consider extending machine learning algorithms to operate on groups of data points. We suggest treating a group of data points as an i.i.d. sample set from an underlying feature dis…

High dimensional data analysis is known to be as a challenging problem. In this article, we give a theoretical analysis of high dimensional classification of Gaussian data which relies on a geometrical analysis of the error measure. It links a problem of classification with a problem of nonparametric regression. We giv…

This work addresses various open questions in the theory of active learning for nonparametric classification. Our contributions are both statistical and algorithmic: -We establish new minimax-rates for active learning under common \textit{noise conditions}. These rates display interesting transitions -- due to the inte…

This paper investigates WDRO for nonparametric regression, achieving robustness against distributional uncertainty.

The paper tightens bounds on covering numbers for deep ReLU networks.

New method estimates tensors from noisy data with missing entries.

Develops a deep learning framework for various data types.