arXiv research

Paper studies $L_2$-consistency of nearest neighbor matching for biased samples.

New methods use vector search and nearest-neighbor matching for policy learning in causal inference.

Study proves NN matching is equivalent to Riesz regression for debiased machine learning.

Nonasymptotic error bounds and strong consistency rates for survival analysis methods.

Optimizes sample reweighting to match laws under covariate shift using Wasserstein distance.

The article proposes modified Gower's coefficients for handling mixed type variables in nearest neighbor methods.

Study nearest-neighbor radii under dependent sampling, finding they remain informative.

Deep nearest neighbors outperform self-supervised methods in anomaly detection.

Generative models use kernel smoothing for conditioning on small example sets.

A new method for covariate shift adaptation using nearest neighbors.

Characterizes Lebesgue points using nearest neighbor methods.

The paper explains how nearest neighbor methods succeed in prediction.

AWNN improves matrix completion by adaptively weighting nearest neighbors.

A new method uses nearest neighbors for importance weighting.

Adaptive algorithm speeds up k-nearest-neighbor searches.

This paper compares FAISS and FENSHSES for nearest neighbor search in Hamming space.

Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle properties. We analyze the b…

The estimation of optimal treatment regimes is of considerable interest to precision medicine. In this work, we propose a causal $k$-nearest neighbor method to estimate the optimal treatment regime. The method roots in the framework of causal inference, and estimates the causal treatment effects within the nearest neig…

A defense against adversarial examples using k-Nearest Neighbor and deep learning.

VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.

New method for clustering binary data using nearest neighbor median shift.

BigNN classifier improves nearest neighbor classification for large datasets.

Paper improves full-text search engines for fast exact NNS in binary codes.

Java implementation improves nearest neighbor algorithm complexity.

The paper tackles time series data by applying conformal prediction with nearest neighbors.

New matching estimators correct bias in multivariate settings without smoothing parameters.

A new multilabel classification framework improves ANN search performance.

K-Nearest neighbor classifier (k-NNC) is simple to use and has little design time like finding k values in k-nearest neighbor classifier, hence these are suitable to work with dynamically varying data-sets. There exists some fundamental improvements over the basic k-NNC, like weighted k-nearest neighbors classifier (wh…

The paper studies empirical processes from nearest neighbors in regression.

This paper proposes a new hashing-based KNN technique for faster nearest neighbor selection.

In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the …

Sublinear memory sketch finds nearest neighbors in streaming data.

The nearest neighbor problem is defined as follows: Given a set $P$ of $n$ points in some metric space $(X,D)$, build a data structure that, given any point $q$, returns a point in $P$ that is closest to $q$ (its "nearest neighbor" in $P$). The data structure stores additional information about the set $P$, which is th…

We propose a simple kernel based nearest neighbor approach for handwritten digit classification. The "distance" here is actually a kernel defining the similarity between two images. We carefully study the effects of different number of neighbors and weight schemes and report the results. With only a few nearest neighbo…

Locally adaptive nearest neighbors improve automated systems' performance and are easier to interpret.

Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear…

New method speeds up k-means clustering for large k by improving nearest-neighbor search.

Improved multiclass classification with class-weighted nearest neighbors.

Improves k-NN for monotonic data with robustness against noise.

The Soft Nearest Neighbor Loss improves representation quality and generalization.

Combines fast evaluation with Bayes consistency in nearest neighbors.

This paper improves nearest neighbor search by learning optimal routing functions.

Study shows $k$-NN regressor consistency in complex survey designs.

Random forest improves nearest neighbor estimation of information-theoretic quantities.

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 …

The stability of statistical analysis is an important indicator for reproducibility, which is one main principle of scientific method. It entails that similar statistical conclusions can be reached based on independent samples from the same underlying population. In this paper, we introduce a general measure of classif…

Adaptive NN method improves matrix completion for non-smooth data.

In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or in face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regressio…