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.
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.
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.
A novel distributed adaptive NN classifier for large data sets.
problem Handling large and distributed data for efficient classification.
method Distributed adaptive nearest neighbor classifier with stochastic tuning parameter selection and early stopping rule.
result Achieves nearly optimal convergence rate under large sub-sample sizes.
Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear, global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However, so far for the references we know, few of which propos…
New algorithm learns from partial labels in general scenarios.
problem Learning from partial labels when each instance has a bag of labels.
method Adaptive nearest-neighbors algorithm PL A-kNN. result PL A-kNN outperforms state-of-the-art methods in general PLL scenarios. A new method for covariate shift adaptation using nearest neighbors.
problem Mitigating distribution shift between source and target datasets.
method Directly work on unlabeled target data, labeled by nearest neighbors in source data.
result Optimal choice of k=1 simplifies hyper-parameter tuning and improves efficiency. 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…
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.
Adaptive multiclass classifier improves accuracy with varying data smoothness.
problem Multiclass classification with unknown continuous functions.
method Fixed nk-nearest neighbors, aggregation, convex combination, adaptation to unknown α. result Aggregated estimate performs as well as an oracle choice, adapts to unknown α. A new method estimates optimal treatment regimes using causal nearest neighbors.
problem Estimating optimal treatment regimes in precision medicine.
method Causal k-nearest neighbor method, with adaptive metric and variable selection.
result The causal k-nearest neighbor regime is universally consistent and converges as sample size increases.
A new ranking algorithm learns data affinity and ranking scores simultaneously.
problem Retrieving similar objects in large databases is challenging.
method Proposes a ranking algorithm that learns data affinity and ranking scores simultaneously, using adaptive neighbors and smoothness constraints.
result The proposed algorithm outperforms existing methods in synthetic and real datasets.
Meta-Neighborhoods adapts predictions based on input neighborhoods.
problem Adaptive prediction based on input neighborhoods for AI.
method Semi-parametric method with induced neighborhoods and meta-learning.
result Meta-Neighborhoods more accurately represents predictive distributions.
SDSPCAAN combines supervised and local data structures for better dimensionality reduction.
problem Preserving both global and local data structures for noisy high-dimensional data.
method Supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN).
result SDSPCAAN improves classification accuracy on high-dimensional datasets.
Improves predictive algorithm performance with domain adaptation.
problem Improves performance of predictive algorithms in distributional shift scenarios.
method Domain adaptive bootstrap aggregating with iterative nearest neighbor sampling.
result Proposes a method to improve predictive algorithm performance in distributional shift scenarios.
DANCE improves prediction set efficiency for deep learning models.
problem Inefficient, overly conservative prediction sets for pre-trained models.
method DANCE combines adaptive kernel regression and nearest-neighbor approach.
result DANCE produces more efficient and robust prediction sets.
The weighted k-nearest neighbors algorithm is one of the most fundamental non-parametric methods in pattern recognition and machine learning. The question of setting the optimal number of neighbors as well as the optimal weights has received much attention throughout the years, nevertheless this problem seems to have r…
DW-KNN improves KNN by integrating distance and neighbor reliability for better prediction accuracy.
problem Standard KNN assumes all neighbors are equally reliable, leading to unreliable predictions in heterogeneous feature spaces.
method DW-KNN integrates exponential distance with neighbor validity, providing instance-level interpretability and reducing hyperparameter sensitivity.
result DW-KNN achieves 0.8988 average accuracy, ranks 2nd among six methods, and has the lowest cross-validation variance.
A new method for density estimation using nearest neighbor Dirichlet mixtures.
problem Slow and unstable Bayesian density estimation methods.
method Nearest neighbor grouping, local Bayesian parametric models, Dirichlet prior, Monte Carlo sampling.
result Effective density estimation with improved computational efficiency.
Adaptive kNN method closes the gap for unbounded support distributions.
problem Inability of standard kNN to achieve minimax optimal rate for unbounded support distributions.
method Adaptive kNN method with varying k for different samples.
result The adaptive kNN method matches the minimax lower bound.
GmCN adapts GCN feature aggregation to improve graph learning.
problem Fixed neighborhood graph aggregation in GCNs is biased and can be affected by graph structure noises.
method GmCN allows nodes to adaptively select optimal neighbors for feature aggregation.
result GmCN improves graph learning effectiveness through adaptive neighbor selection.
PG-EVIKAL refines molecular property predictions using neighbor fusion and evidential neural networks.
problem Improving molecular property predictions using test-time neighbor fusion.
method Adapting evidential neural networks to refine predictions by re-ranking structurally similar neighbors.
result PG-EVIKAL reduces RMSE on 14 out of 16 molecular datasets, improving calibration and sequential refinement.
This work proposes ACTC for adaptive distributed learning under communication constraints.
problem Adaptive distributed learning in networks with communication constraints.
method ACTC (Adapt-Compress-Then-Combine) strategy with diffusion exchange of compressed updates.
result ACTC iterates converge to the optimizer with significant bit savings.
Adaptive k-NN classifier improves accuracy over fixed k-NN.
problem Improving classification accuracy by dynamically choosing k.
method Adaptive selection of k based on local neighborhood properties.
result The adaptive k-NN classifier performs comparably to or better than fixed k-NN.
KL nearest neighbor estimator achieves near-minimax rates for differential entropy.
problem Estimating differential entropy with Hölder smoothness.
method Uniform upper and lower bounds on KL estimator performance.
result KL estimator is near-minimax rate-optimal without knowing smoothness.
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.
Proposes a method to learn optimal neighbors and projection matrix in low-dimensional space.
problem Difficulty in precisely measuring similarity and selecting optimal neighbors in high-dimensional space.
method Models similarity and neighbors as variables, optimizing a unified objective function with nonnegative and sum-to-one constraints.
result Optimal similarity and projection matrix learned simultaneously, with adaptive regularization parameter.
New algorithm finds nearest neighbors with less computation.
problem Finding nearest neighbors in high-dimensional data efficiently.
method Bandit-based Monte Carlo optimization for nearest neighbors.
result Algorithm identifies exact nearest neighbors with high probability.
Study exact community recovery in noisy SBM with limited queries.
problem Community recovery in noisy stochastic block models with limited queries.
method Balanced uniform querying, two-stage adaptive strategy, sublinear queries, subsampled graph.
result Adaptive querying can improve exact recovery limits in noisy SBM.
The paper investigates learning conditional distributions on multi-dimensional spaces using clustering and neural networks.
problem Learning conditional distributions on multi-dimensional spaces with varying dimensions.
method The approach involves clustering data near varying query points in the feature space to create empirical measures in the target space using two clustering schemes: fixed-radius ball and nearest neighbors. The convergence rates of both methods are analyzed, and the nearest neighbors method is incorporated into neural network training.
result The empirical analysis shows that the nearest neighbors method has better performance in practice and can adapt to a suitable level of Lipschitz continuity locally.
Paper proposes deep learning models for k-NN classification.
problem Imbalanced datasets and complex feature vectors in classification.
method Sequence to sequence model and memory network models.
result Models outperform k-NN and other state-of-the-art models.
BaNk-UCB tackles batched nonparametric bandits with k-NN regression and UCB.
problem Sequential decision-making with limited online feedback in domains like medicine and marketing.
method Combines k-NN regression with UCB principle for fully nonparametric, adaptive, and simple implementation.
result Near-optimal regret guarantees under Lipschitz smoothness and margin assumptions, with minimax-optimal rates.
A new method for learning distance metrics for K-NN classification.
problem Improving the performance of K-NN classifier by learning an appropriate distance metric.
method Designing a continuous decision function for K-NN and minimizing its continuous empirical risk function.
result The proposed ANN algorithm outperforms existing methods like LMNN, NCA, and pairwise constraints.
New algorithms detect outliers in high-dimensional data with arbitrary shapes.
problem Challenges of high dimensionality and varying cluster shapes in traditional outlier detection methods.
method Cluster Catch Digraphs (CCDs) and their variants (U-MCCD, UN-MCCD, SU-MCCD, SUN-MCCD).
result U-MCCD efficiently identifies outliers with high true negative rates, and SU-MCCD improves handling of non-uniform clusters.
Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lea…
A new test for conditional independence adapts to nonlinear dependencies efficiently.
problem Testing conditional independence in nonlinear and high-dimensional data.
method Nearest-neighbor estimator of conditional mutual information combined with local permutation scheme.
result The test reliably simulates null distribution and is better calibrated for non-smooth densities.
Acoustic Neighbor Embeddings map speech and text to fixed dimensions for phonetic confusability.
problem Mapping speech and text to fixed dimensions for phonetic confusability.
method Adapting SNE to sequential inputs, training two encoder neural networks.
result More accurate results with low-dimensional embeddings in word recognition tasks.
New method for estimating counterfactual means in adaptive experiments.
problem Inference for counterfactual means in sequentially designed experiments with adaptive treatment policies.
method Latent factor model and nearest neighbors method for estimation.
result Asymptotically valid confidence intervals for counterfactual means established.
Generative models use kernel smoothing for conditioning on small example sets.
problem Improving generative models' performance with limited conditioning examples.
method Showed that cross-attention conditioning is equivalent to kernel smoothing, specifically a Nadaraya--Watson kernel smoother.
result The approach predicts and confirms three failure regimes for kernel-based conditioning.
A new method for online multi-label stream classification.
problem Challenges in classifying continuous data streams with concept drift and delayed labels.
method Online unsupervised incremental method based on self-organizing maps.
result The method is highly competitive in both stationary and concept drift scenarios.
DADC algorithm improves clustering for data with varying density.
problem Sparse cluster loss and cluster fragmentation in density peak clustering.
method Domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble.
result DADC achieves more reasonable clustering results on data with varying density.
SPlit optimizes dataset splitting for better model performance.
problem Improving model performance through optimal dataset splitting.
method Adapting Support Points (SP) algorithm for subsampling and categorical variables in a sequential nearest neighbor approach.
result SPlit significantly improves worst-case testing performance compared to random splitting.
Two methods forecast functional time series, offering competitive results.
problem Forecasting functional time series with model-free approaches.
method Two nonparametric methods: k-nearest neighbors adaptation and curve envelope selection.
result Competitive results with and often superior to benchmarks.
CGMMD generates conditional samples in one shot with MMD and nearest neighbors.
problem Generating samples from a conditional distribution unseen in training.
method CGMMD uses MMD for a direct minimization problem, producing samples in one forward pass.
result CGMMD achieves convergence to the true conditional distribution with theoretical bounds.
Improves k-NN for monotonic data with robustness against noise.
problem Class noise in real-life data violates monotonic constraints in k-NN.
method Monotonic Fuzzy k-NN (MonFkNN) with new fuzzy membership calculation.
result Significant accuracy improvements and robustness against monotonic noise.
Recent theory work has found that a special type of spatial partition tree - called a random projection tree - is adaptive to the intrinsic dimension of the data from which it is built. Here we examine this same question, with a combination of theory and experiments, for a broader class of trees that includes k-d trees…
Proposes a graph learning framework for clustering and semi-supervised classification.
problem Graph-based clustering and semi-supervised classification techniques have shown impressive performance but lack explicit cluster structure.
method Uses self-expressiveness of samples for global structure and adaptive neighbor approach for local structure. Incorporates rank constraint to ensure optimal performance.
result The proposed method achieves better performance than state-of-the-art methods in clustering and semi-supervised classification.
Paper develops multivariate time series similarity and distance measures.
problem Compensating for misalignments in multivariate time series data.
method Adapted Independent and Dependent DTW strategies to seven elastic similarity and distance measures.
result Each measure achieves highest accuracy on at least one dataset, supporting their value.