arXiv research

Proposes SD-KDE for density estimation using debiased kernel density with score-based adjustments.

While robust parameter estimation has been well studied in parametric density estimation, there has been little investigation into robust density estimation in the nonparametric setting. We present a robust version of the popular kernel density estimator (KDE). As with other estimators, a robust version of the KDE is u…

This work addresses dynamic KDE data structures with robustness to adversarial queries.

Geometry-aware KDE model improves multiclass quantification.

We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic repr…

Consistency of the kernel density estimator requires that the kernel bandwidth tends to zero as the sample size grows. In this paper we investigate the question of whether consistency is possible when the bandwidth is fixed, if we consider a more general class of weighted KDEs. To answer this question in the affirmativ…

New density estimator from Markov Chains outperforms KDE.

Paper proposes a hybrid model for VaR forecasting using SVR, GARCH, and KDE.

Paper introduces MoM-KDE for robust density estimation robust to anomalous data.

New sublinear sketches improve ANN and KDE for massive data streams.

DKGM improves image quality by debiasing kernel-based models.

Paper bridges VAEs and KDEs for more flexible posterior estimation.

We study the problem of structured output learning from a regression perspective. We first provide a general formulation of the kernel dependency estimation (KDE) problem using operator-valued kernels. We show that some of the existing formulations of this problem are special cases of our framework. We then propose a c…

WS-KDE provides robust confidence bounds for stochastic functions.

Solving real-world problems, particularly with deep learning, relies on the availability of abundant, quality data. In this paper we develop a novel framework that maximises the utility of time-series datasets that contain only small quantities of expertly-labelled data, larger quantities of weakly (or coarsely) labell…

New algorithm improves data imputation for complex multimodal data sets.

Optimizes kernel density ratios for better predictions and information measures.

We introduce a balloon estimator in a generalized expectation-maximization method for estimating all parameters of a Gaussian mixture model given one data sample per mixture component. Instead of limiting explicitly the model size, this regularization strategy yields low-complexity sparse models where the number of eff…

Modal regression estimates the local modes of the distribution of $Y$ given $X=x$, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of …

Study guarantees convergence of mean shift mode estimation.

Imbalanced response variable distribution is a common occurrence in data science. In fields such as fraud detection, medical diagnostics, system intrusion detection and many others where abnormal behavior is rarely observed the data under study often features disproportionate target class distribution. One common way t…

The paper provides consistency results for KDE on manifolds with irregular kernels.

Kernel learning FBSDE filter improves nonlinear filtering efficiency.

A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a …

A new KDE-based method improves multiclass quantification.

Distinguishing between classes of time series sampled from dynamic systems is a common challenge in systems and control engineering, for example in the context of health monitoring, fault detection, and quality control. The challenge is increased when no underlying model of a system is known, measurement noise is prese…

We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density estimator (KDE) with ideas from classical $M$-estimation. We interpret the KDE based on a radial, positive semi-definite ke…

A new KDE model prevents singular solutions and accelerates optimization for probabilistic modeling.

A new method for Bayesian inference tackles high-dimensional problems.

The article derives a novel Gram-Charlier A (GCA) Series based Extended Rule-of-Thumb (ExROT) for bandwidth selection in Kernel Density Estimation (KDE). There are existing various bandwidth selection rules achieving minimization of the Asymptotic Mean Integrated Square Error (AMISE) between the estimated probability d…

The paper studies kernel smoothing and mean shift for directional data, deriving convergence rates and mode estimation.

In this paper, a nonparametric maximum likelihood (ML) estimator for band-limited (BL) probability density functions (pdfs) is proposed. The BLML estimator is consistent and computationally efficient. To compute the BLML estimator, three approximate algorithms are presented: a binary quadratic programming (BQP) algorit…

ROME improves density estimation for multi-modal, non-normal data.

We propose a generic spatiotemporal event forecasting method, which we developed for the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian …

Improved DP KDE with better privacy and efficiency.

A new noise model for preferential Bayesian optimization using user anchors.

New method uses machine learning to estimate sensitivity without binning.

LLMs learn probability density functions in-context, showing distinct learning trajectories.

Neural networks have been widely used as predictive models to fit data distribution, and they could be implemented through learning a collection of samples. In many applications, however, the given dataset may contain noisy samples or outliers which may result in a poor learner model in terms of generalization. This pa…

This paper improves bandwidth selectors for SPBNs to enhance their performance.

LGKDE learns graph density using neural networks and perturbations.

CoDrug uses KDE to create valid prediction sets for drug molecules under covariate shift.

Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational …

Efficient clustering in high dimensions with Quick Shift and LSH.

Unified framework for data-free sampling using Wasserstein gradient flows.

A probability density function (pdf) encodes the entire stochastic knowledge about data distribution, where data may represent stochastic observations in robotics, transition state pairs in reinforcement learning or any other empirically acquired modality. Inferring data pdf is of prime importance, allowing to analyze …

In recent years there has been noticeable interest in the study of the "shape of data". Among the many ways a "shape" could be defined, topology is the most general one, as it describes an object in terms of its connectivity structure: connected components (topological features of dimension 0), cycles (features of dime…