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.
Quantum-enhanced barcode decoding and pattern recognition outperforms classical methods.
problem Improving barcode decoding and pattern recognition using quantum entanglement.
method Quantum hypothesis testing applied to barcode decoding and pattern recognition using entangled quantum sources and measurements.
result Quantum-enhanced methods outperform classical coherent-state strategies for barcode data decoding and classification.
A new algorithm improves multi-label classification performance across various cost functions.
problem Multi-label classification performance varies with different cost functions.
method Proposes cost-sensitive label embedding with multidimensional scaling (CLEMS) that approximates cost information using distances of embedded vectors.
result CLEMS significantly outperforms existing algorithms across different cost functions.
Proposes autoencoding with random forests using spectral graph theory.
problem Learning low-dimensional embeddings of random forest models.
method Combines nonparametric statistics and spectral graph theory for optimization.
result Establishes a universal consistent decoder for random forest models.
A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of inform…
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.
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.
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.
Efficiently find approximate nearest neighbors in high dimensions.
problem Finding the closest point in a high-dimensional dataset.
method Develops efficient data structures for approximate nearest neighbor search.
result Efficient solutions for approximate nearest neighbor problem.
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…
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.
Improved nearest neighbor classification for time series data.
problem High storage and computation requirements for large training sets in DTW space.
method Extends LVQ to DTW spaces using asymmetric weighted averaging.
result Asymmetric GLVQ outperforms other methods in nearest neighbor classification.
Bayesian methods improve kernel and mutual k-nearest neighbor regression.
problem Improving nonparametric regression methods for better accuracy and hyperparameter selection.
method Bayesian extensions of kernel and mutual k-nearest neighbor regression methods based on Gaussian process models.
result The proposed methods asymptotically converge to the original methods and perform better or equally well in simulations.
Interpolated nearest neighbor algorithms minimize bias in machine learning models.
problem Understanding and reducing overfitting in machine learning models.
method Proves the interpolated nearest neighbor algorithm achieves minimax optimal rates in regression and classification.
result Interpolated nearest neighbor algorithms are statistically optimal and perform better than traditional methods in some cases.
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 studies L2-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 L2-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 O(nK2logK(n)) for K-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.
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.
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.
New estimator uses k-nearest neighbor distances for density functionals.
problem Estimating general density functionals from data.
method Asymptotically unbiased estimator using inverse Laplace transform.
result Established L2-consistency and mean squared error convergence. As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individual…
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.
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.
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.
Deep learning method for brain CT scan anomaly labeling using nearest neighbors.
problem Automated anatomical labeling of brain CT scan anomalies.
method Combines local and global context, uses Relation Networks (RNs) for prediction, and employs nearest neighbors for training.
result Improved performance of Relation Networks (RNs) through nearest neighbors training strategy.
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.
Novel low-rank neural decoder improves μ-ECoG neural decoding.
problem Challenging neural decoding from high-dimensional μ-ECoG data. method Low-rank structure in neural network decoder.
result Low-rank decoder outperforms standard PCA.
The paper introduces FMCI and hybrid decoding for hidden Markov models.
problem Computing distributions and decoding hidden state sequences in HMMs.
method Finite Markov chain imbedding (FMCI) and hybrid decoding.
result Hybrid decoding improves performance over traditional methods.
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.
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 k-NN regressor consistency in complex survey designs.
problem Lack of consistency results for k-NN regressor in complex survey data. method Analysis of regularity conditions on sampling design and data distribution.
result Consistency of k-NN regressor under complex survey designs. Paper defines interpretability for brain decoding models.
problem Lack of formal definition and quantitative measure for interpretability in brain decoding.
method Proposes a simple definition and combines interpretability with performance into a new criterion for model selection.
result Optimizing hyper-parameters based on the new criterion yields more informative linear models.
Study examines how decoding algorithms affect fairness in language generation models.
problem Impact of decoding algorithms on fairness in open-ended language generation.
method Systematic analysis of top-p, top-k, and temperature decoding algorithms. result Decoding algorithms significantly impact fairness across demographic groups.
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.
Paper uses RL to optimize bit-flipping decoding for binary codes.
problem Improving bit-flipping decoding for binary linear codes.
method Mapped iterative decoding algorithms to MDPs for reinforcement learning.
result Learned BF decoders offer performance-complexity trade-offs and near-optimal performance.