NPC tackles class imbalance in datasets by progressively considering more neighbors.
problem Class imbalance in datasets causes poor performance in classification tasks.
method Inspired by k-NN, NPC progressively considers more neighbors and uses local-global grading.
result NPC outperforms five representative algorithms on fifteen imbalanced datasets.
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.
New method uses nearest neighbors quantile filter for probabilistic energy forecasting.
problem Creating accurate probabilistic energy forecasts using complex data mining techniques.
method Uses a new nearest neighbors quantile filter to create quantile regressions without a non-differentiable cost function.
result Demonstrates superior performance in Global Energy Forecasting Competition 2014.
Framework predicts patient risk progression over time.
problem Predicting how patient risk changes over time.
method Supervised contrastive learning framework with embedding properties.
result Framework outperforms baselines in mortality and cognitive impairment datasets.
Indirect competition emerged from the complex organization of human societies, and knowledge of the existing network topology may aid in developing effective strategies for success. Here, we propose an agent-based model of competition with systems co-existing in a `small-world' social network. We show that within the r…
LIDL estimates local intrinsic dimension in high dimensions.
problem Estimating local intrinsic dimension in high-dimensional data.
method Approximate likelihood using parametric neural density estimation.
result LIDL scales to thousands of dimensions and yields competitive results.
NIPS 2018 Adversarial Vision Challenge aims to improve machine vision models.
problem Improving robustness of machine vision models and adversarial attacks.
method Organized a competition to measure progress in adversarial machine vision.
result Facilitated measurable progress in adversarial machine vision.
A new classifier encodes local neighborhoods for each class using Fly Bloom Filters.
problem Efficiently classify data with single-pass learning.
method Proposes a new classifier that encodes local neighborhoods for each class with per-class Fly Bloom Filters.
result The proposed classifier's performance is competitive with nearest-neighbor classifiers and other single-pass classifiers.
Unsupervised classifier performs as well as supervised ones on ImageNet dataset.
problem Achieving performance of supervised learning classifiers without labeled data.
method Incremental shift and rotation operations on selected hyperplanes.
result 6.2% Top 3 probability of error on ImageNet dataset.
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.
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.
Improved career progression analysis in baseball using machine learning.
problem Simplistic approaches to analyzing career progression in baseball.
method Applied four machine learning algorithms to batting data.
result Soundly improved upon existing approaches, particularly for batting data.
One-pass private sketch supports various machine learning tasks.
problem Efficiently supporting multiple machine learning tasks with differential privacy.
method Randomized contingency tables indexed with locality-sensitive hashing, constructed in one pass.
result Competitive error bounds for DP kernel density estimation, faster than existing methods.
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.
Method predicts spinal deformity progression using 3D models and machine learning.
problem Predicting the progression of spinal deformities in scoliosis patients.
method Discriminative probabilistic manifold embedding for 3D spine models.
result 81% classification rate and 2.1° prediction difference in curve angulation.
We present a linear agent based model on brand competition. Each agent belongs to one of the two brands and interacts with its nearest neighbors. In the process the agent can decide to change to the other brand if the move is beneficial. The numerical simulations show that the systems always condenses into a state when…
ASK-NN detects distribution drifts in LLM-generated text.
problem Hallucinations and artificial text in LLM-generated outputs.
method Asymmetric two-sample test based on directed k-nearest-neighbor graph.
result ASK-NN is competitive with baselines on various benchmarks.
A new graph-based clustering method for moderate-dimensional data.
problem Performance degradation of existing graph-based clustering methods in high dimensions.
method Introduces UN-CCDs using NND-based MC-SRT for covering radii determination.
result UN-CCDs provide stable and competitive performance in moderate-sized datasets.
Enhances classifier performance through feature space transformations and model selection.
problem Improving the accuracy of classifiers by reducing complexity.
method Combining feature mapping, prototype selection, and kernel function transformations to transform data into a more convenient distribution.
result Our methods produce competitive classifiers and are statistically different among them.
Study finds apartment owners can set Airbnb policies to be socially efficient.
problem Negative externalities from Airbnb hosts on neighbors.
method Modeling competitive rental market and apartment sorting.
result Equilibrium Airbnb fraction is socially efficient if tenants can sort.
A comparison-based algorithm finds nearest neighbors in metric spaces.
problem Finding nearest neighbors without direct distance information.
method Recursive splitting using random pivot points to form a comparison tree.
result The height of the comparison tree is logarithmic in the number of points, leading to efficient search performance.
This paper provides an attempt to formalize Hayek's notion of spontaneous order within the framework of the Arrow-Debreu economy. Our study shows that if a competitive economy is enough fair and free, then a spontaneous economic order shall emerge in long-run competitive equilibria so that social members together occup…
A reject option improves partial-label learning's accuracy.
problem Ambiguously labeled data in real-world applications.
method Risk-consistent nearest-neighbor algorithm with a reject option.
result Our method provides the best trade-off between non-rejected predictions' number and accuracy.
DRL optimizes complex mobile networks with imperfect info.
problem Optimizing mobile networks with scarce data and complex dynamics.
method Sim-to-Real framework using graph CNN, domain randomization, multi-task learning, and self-play.
result First successful transfer of DRL from simulation to real-world mobile networks.
Algorithm finds adversarial examples for k-NN classifiers using Voronoi diagrams.
problem Ensuring robustness of k-NN classifiers against adversarial attacks.
method Geometric approach expanding outwards from input points to find minimum-norm adversarial examples.
result Our method outperforms existing approaches on various datasets.
A competition revealed that flexible modeling outperforms rigid methods in causal inference.
problem Comparing new causal inference methods to established ones in a realistic setting.
method 30 competitors submitted methods, some flexible, some rigid, tested on real data.
result Flexible modeling methods outperformed rigid methods overall.
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…
A novel unsupervised outlier detection method using Randomized PCA Forest.
problem Unsupervised outlier detection in datasets.
method Randomized Principal Component Analysis (RPCA) Forest for deriving an outlier score.
result Superior performance compared to classical and state-of-the-art methods.
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
PNN-smoothing improves k-means clustering by merging subsets' clusterings.
problem Improving k-means clustering initialization efficiency and effectiveness. method Split dataset into subsets, cluster each subset, merge with PNN method.
result PNN-smoothing enhances k-means++ seeding, reducing costs. CRAD clusters data with robust depth-based dissimilarity, outperforming existing methods.
problem Clustering data with varying densities and unknown true number of clusters.
method CRAD uses a robust data depth as dissimilarity measure and a novel neighbor searching function.
result CRAD outperforms DBSCAN, OPTICS, and DBCA in detecting clusters with varying densities.
FastMuyGPs speeds up GP predictions for large datasets.
problem High cost of Gaussian process predictions for large data.
method Combines cross-validation, batching, nearest neighbors sparsification, and precomputation.
result Superior accuracy and competitive runtime compared to other methods.
Unified Python package N2 benchmarks NN-based matrix completion methods.
problem Matrix completion with robust NN methods across diverse applications.
method Unified Python package N2 consolidates NN-based methods. result NN-based techniques outperform classical methods in real-world settings.
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.
Proposes interpretable time series classification through extracted features.
problem Interpretable time series classification in complex problems.
method Extracts features from time series to improve interpretability of traditional classifiers.
result No statistically significant differences in accuracy compared to state-of-the-art models.
C-kNN-LSH identifies similar patient histories for causal inference in longitudinal data.
problem Estimating causal effects from longitudinal trajectories with high-dimensional confounding.
method C-kNN-LSH uses locality-sensitive hashing to find clinical twins and estimate treatment effects.
result C-kNN-LSH outperforms existing methods in capturing recovery heterogeneity and estimating policy values.
Progressive stochastic binarization improves deep network inference efficiency.
problem Efficient inference of deep networks with reduced memory and computational resources.
method A progressive stochastic binarization scheme for deep networks that uses small integers and fixed shifts.
result Matches the accuracy of previous binarized approaches and reduces inference costs by up to 33%.
Locally-contextual CRFs improve sequence labeling performance.
problem Improving sequence labeling with contextual embeddings.
method Locally-contextual nonlinear CRFs using deep neural networks.
result Consistently outperforms linear chain CRF and previous state of the art.
Improved GANs produce high-quality images with stable training and increased variation.
problem Training GANs for high-quality images with stability and diversity.
method Progressive growth of generator and discriminator, adding layers progressively.
result Achieved unprecedented image quality and record inception score.
Bayesian meta-learning predicts Alzheimer's disease progression.
problem Predicting individual Alzheimer's disease progression from limited data.
method Bayesian meta-learning approach that dynamically predicts disease score distributions.
result Bayesian meta-learner outperforms single-task models and deterministic meta-learners, especially for long-term predictions.
Study shows competition feedback can make ML predictors biased towards specific user groups.
problem How competition affects machine learning predictors and user prediction quality.
method Flexible model of competing ML predictors, empirical and mathematical analysis.
result Competition causes predictors to specialize for specific sub-populations at the cost of general performance.
New method uses MHN for associative learning in network embedding.
problem Represent nodes in networks as low-dimensional vectors while incorporating topological and structural information.
method Introduces Modern Hopfield Networks (MHN) for associative learning between node content and neighbors.
result Competitive performance on node classification and linkage prediction tasks.
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.
Random forest method improves label ranking performance.
problem Label ranking from instances to rankings over predefined labels.
method Random decision trees and rank aggregation strategy.
result New random forest method achieves competitive performance.
User smeznar achieved 8th place in PGDL by predicting generalization of deep learning models.
problem Understanding and predicting generalization in deep learning models.
method Creating simple metrics and finding their best combination for automatic testing on a dataset.
result Combination of various properties of neural network architectures can be used for generalization prediction.
A new robust time series distance metric for k-NN classification.
problem Robustness against arbitrary data contamination in time series classification.
method Proposes a novel distance metric with worst-case O(nlogn) complexity. result Demonstrates competitive classification accuracy in k-NN time series classification.
Graphs grow with preferential attachment, incorporating communities.
problem Modeling growth of networks with community structure.
method Progressive attachment with label-based preferential attachment.
result Empirical degree distribution for labeled vertices is heavy-tailed.
We focus in this paper on dataset reduction techniques for use in k-nearest neighbor classification. In such a context, feature and prototype selections have always been independently treated by the standard storage reduction algorithms. While this certifying is theoretically justified by the fact that each subproblem …