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…
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48 results for “latent nearest neighbors”
Generative Latent Nearest Neighbors (GLANN) generates better images without adversarial training.
problem Stability and mode collapse issues in GANs for image generation.
method Combines IMLE and GLO strengths to avoid GAN's weaknesses.
result Generative Latent Nearest Neighbors (GLANN) generates better images than GLO and IMLE.
Improved nearest neighbors for missing data in latent factor models.
problem Estimation with missing data in latent factor models.
method Doubly robust nearest neighbors (NN) that considers both row and column neighbors.
result Provides a consistent estimate and near-quadratic improvement in error.
Exemplar VAEs link generative models with nearest neighbor retrieval and data augmentation.
problem Improving generative model performance and data augmentation effectiveness.
method Exemplar VAEs with Parzen window prior, retrieval augmented training, exemplar leave-one-out, and subsampling.
result Generative data augmentation reduces classification error on MNIST and Fashion MNIST.
For classifying time series, a nearest-neighbor approach is widely used in practice with performance often competitive with or better than more elaborate methods such as neural networks, decision trees, and support vector machines. We develop theoretical justification for the effectiveness of nearest-neighbor-like clas…
VAEs can generate novel examples but performance varies with latent space dimensionality.
problem Can VAEs generate novel examples not seen in training data?
method Investigated VAEs on MNIST dataset, varying latent space dimensions.
result Higher-dimensional latent spaces lead to better nearest-neighbor approximations, but lower dimensions offer an advantage for unseen classes.
Study nearest-neighbor radii under dependent sampling, finding they remain informative.
problem Analyzing nearest-neighbor radii under dependent sampling.
method Consider strong mixing dependent observations, establish distribution-free almost sure convergence and sharp non-asymptotic moment bounds.
result Nearest-neighbor geometry remains informative under dependence sampling.
Deep nearest neighbors outperform self-supervised methods in anomaly detection.
problem Anomaly detection using self-supervised deep methods.
method Simple nearest-neighbor approach on Imagenet pretrained features.
result Nearest-neighbor method outperforms self-supervised methods in accuracy, few shot generalization, training time, and noise robustness.
Characterizes Lebesgue points using nearest neighbor methods.
problem Consistency of classification algorithms based on nearest neighbors.
method Characterization of Lebesgue points via 1-Nearest Neighbor regression.
result Proves convergence of 1-Nearest Neighbor classification algorithms in metric spaces.
The paper explains how nearest neighbor methods succeed in prediction.
problem Explaining the success of nearest neighbor methods in prediction.
method The paper covers both theoretical and practical aspects of nearest neighbor methods, including statistical guarantees and practical algorithms.
result The paper provides nonasymptotic statistical guarantees and practical algorithms for nearest neighbor methods.
AWNN improves matrix completion by adaptively weighting nearest neighbors.
problem Matrix completion with optimal nearest neighbor weights and radii selection.
method Adaptively weighted nearest neighbor method for matrix completion.
result Theoretical guarantees and synthetic experiments support the effectiveness of AWNN.
A new method uses nearest neighbors for importance weighting.
problem Data covariate shift problems in machine learning.
method Nearest neighbor classification scheme for determining importance weights.
result Demonstrated effectiveness through comparative experiments on various classification tasks.
Adaptive algorithm speeds up k-nearest-neighbor searches.
problem Finding k nearest neighbors with varying efficiency.
method Adaptive estimation of distances to optimize search.
result The algorithm achieves significant speedups compared to naive methods.
This paper compares FAISS and FENSHSES for nearest neighbor search in Hamming space.
problem Comparing nearest neighbor search systems in Hamming space.
method Comprehensive evaluations of indexing speed, search latency, and RAM consumption.
result Better understanding of trade-offs between main memory and secondary memory systems.
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…
Adaptive NN method improves matrix completion for non-smooth data.
problem Matrix completion with non-smooth non-linear functions under high missingness.
method Two-sided nearest neighbors with \Holder function class non-linearity.
result NN error rate matches oracle's for latent factors, non-trivial for wide range of missingness.
The estimation of optimal treatment regimes is of considerable interest to precision medicine. In this work, we propose a causal -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.
problem Evaluating robustness of k-Nearest Neighbor and its deep learning combination.
method Proposed heuristic attack to find adversarial examples for kNN and DkNN.
result Our attack significantly outperforms other attacks on DkNN.
VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.
problem Scalability issues in Gaussian process approximations.
method Sparse precision structure via nearest neighbors, variational framework.
result VNNGP outperforms low-rank methods and is less prone to overfitting.
New method for clustering binary data using nearest neighbor median shift.
problem Clustering binary data effectively.
method BinNNMS based on nearest neighbor median shift.
result BinNNMS accurately discovers cluster locations in binary data.
Paper explains distance-based classifiers using neural network structures.
problem Making distance-based classifiers explainable.
method Uncovering latent neural network structures in distance-based classifiers.
result Novel explanation approach outperforms baselines.
BigNN classifier improves nearest neighbor classification for large datasets.
problem Classification of large datasets that cannot fit into a single machine's memory.
method Divide and conquer scheme with majority voting for final decision; pre-training acceleration technique.
result Rates of convergence for bigNN classifier under minimal assumptions, proving it as optimal.
Paper studies -consistency of nearest neighbor matching for biased samples.
problem Biased sampling and missing data in statistical problems.
method Nearest neighbor matching (NNM) to estimate population quantities from biased samples.
result NNM is -consistent in finite dimensions without smoothness and boundedness assumptions.
Java implementation improves nearest neighbor algorithm complexity.
problem Improving efficiency of nearest neighbor descent algorithm.
method Parallel streams implementation with statistical termination criterion.
result Complexity up to for -nearest neighbors.
The paper tackles time series data by applying conformal prediction with nearest neighbors.
problem Time series data violates the exchangeability assumption required for conformal prediction.
method The approach uses the nearest neighbors method with fast parameter tuning and weighted nearest neighbors (FPTO-WNN) to construct reliable prediction intervals.
result Data analysis shows the effectiveness of the proposed approach.
Improved clustering accuracy with disentangled latent code representation.
problem Improving k-Means clustering performance.
method Optimizing the entanglement of autoencoder latent code representation using soft nearest neighbor loss with annealing temperature.
result 96.2% test clustering accuracy on MNIST, 85.6% on Fashion-MNIST, and 79.2% on EMNIST Balanced datasets.
A new multilabel classification framework improves ANN search performance.
problem Efficiently finding approximate nearest neighbors in large datasets.
method Formulated ANN search as a multilabel classification problem, using partitioning classifiers.
result Natural classifier leads to strictly improved performance in ANN search.
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.
problem Estimating conditional cumulative distribution functions and local linear regression.
method Uniform central limit theorem and non-asymptotic bound under local bracketing entropy and uniform entropy numbers.
result Gaussian limit of empirical process with simple covariance.
This paper proposes a new hashing-based KNN technique for faster nearest neighbor selection.
problem Slowness of KNN in big datasets due to searching entire dataset.
method Divide data space into subcells, use hashing to map data points, and select nearest neighbors layer by layer.
result The proposed technique offers competitive performance with KNN and KDtree while significantly improving time efficiency.
New methods use vector search and nearest-neighbor matching for policy learning in causal inference.
problem Learning optimal policies in causal inference with limited data.
method RAG-based policy learning with vector search and nearest-neighbor matching.
result The methods bound the within-candidate choice regret and evaluate the one-step method directly as a policy.
Sublinear memory sketch finds nearest neighbors in streaming data.
problem Finding nearest neighbors in large datasets with limited memory.
method Combines LSH, online kernel density estimation, and compressed sensing to achieve sublinear memory.
result Achieves sublinear memory performance on stable queries, reporting nearest neighbors efficiently.
The nearest neighbor problem is defined as follows: Given a set of points in some metric space , build a data structure that, given any point , returns a point in that is closest to (its "nearest neighbor" in ). The data structure stores additional information about the set , 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.
problem Improving automated systems' performance and interpretability.
method Developed a method for k nearest neighbors algorithms to learn locally adaptive metrics.
result Locally adaptive metrics improve performance and are interpretable.
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the …
New method speeds up k-means clustering for large k by improving nearest-neighbor search.
problem Efficiently clustering large datasets with high-dimensional points.
method Seeded Approximate Nearest-Neighbor Search methods to improve Lloyd's algorithm.
result Significantly faster k-means clustering for large k values.
Improved multiclass classification with class-weighted nearest neighbors.
problem Multiclass classification with large or imbalanced classes.
method Class-weighted k-nearest neighbors algorithm, derived bounds on accuracy and risk.
result Optimized classification metrics like F1 score or Matthew's Correlation Coefficient.
The Soft Nearest Neighbor Loss improves representation quality and generalization.
problem Improving the quality of representations in machine learning models.
method Explored and expanded the Soft Nearest Neighbor Loss to measure class entanglement.
result Maximizing the entanglement of different class representations leads to better generalization and uncertainty estimation.
Combines fast evaluation with Bayes consistency in nearest neighbors.
problem Balancing fast evaluation time with Bayes consistency in nearest neighbors.
method Combines locality-sensitive hashing (LSH) with a missing-mass argument.
result Fast and Bayes-consistent classifier with comparable risk decay rates.
Method uses optimal transport to complete distributional matrices.
problem Matrix completion for distributional data.
method Nearest neighbors in Wasserstein space.
result Method recovers distributions in Wasserstein metric.
Automatically tunes hyperparameters for faster approximate nearest neighbor search.
problem Tuning hyperparameters for efficient approximate nearest neighbor search is slow and impractical.
method Proposes an algorithm using randomized space-partitioning trees to automatically tune hyperparameters.
result Significantly faster than existing approaches and competitive in query time.
This paper improves nearest neighbor search by learning optimal routing functions.
problem Local minima issues in greedy routing on similarity graphs.
method Learn routing function that considers global graph structure.
result Significant improvement in search performance via learning.
Study shows -NN regressor consistency in complex survey designs.
problem Lack of consistency results for -NN regressor in complex survey data.
method Analysis of regularity conditions on sampling design and data distribution.
result Consistency of -NN regressor under complex survey designs.
Nonasymptotic error bounds and strong consistency rates for survival analysis methods.
problem Establishing reliable error bounds and consistency rates for survival analysis methods.
method Nonasymptotic error bounds for Kaplan-Meier-based nearest neighbor and kernel survival probability estimators in metric spaces.
result Rates of strong consistency match existing lower bounds for conditional CDF estimation.
Random forest improves nearest neighbor estimation of information-theoretic quantities.
problem Estimating information-theoretic quantities in high-dimensional and scale-different settings.
method Decision forest-based adaptive nearest neighbor estimators.
result Forest-based estimators effectively estimate posterior probabilities and mutual information.
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…