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.
NNK algorithm improves neighborhood and graph construction for machine learning.
problem Ad hoc selection of k and ε parameters in kNN and ε-neighborhood methods.
method NNK algorithm for better sparse signal approximation.
result NNK leads to superior performance in local neighborhood and graph-based machine learning tasks.
An LSTM-based approach predicts graph nodes based on local neighborhood and node features.
problem Predicting graph nodes using local neighborhood and node features.
method Multi-level architecture based on LSTMs that learn to summarize neighborhoods from data.
result Effectiveness demonstrated on synthetic and real-world data.
MixHop learns complex neighborhood relationships in graphs.
problem Existing graph neural networks cannot learn certain neighborhood mixing relationships.
method MixHop repeatedly mixes feature representations of neighbors at various distances.
result MixHop outperforms on challenging baselines and visualizes neighborhood information prioritization.
GraphAIR improves graph representation learning by capturing non-linear interactions.
problem Challenges in capturing non-linear interactions in graph data.
method Integrates neighborhood aggregation and interaction modeling.
result Demonstrates improved performance on node classification and link prediction tasks.
Urban2Vec combines street view imagery and POIs for better urban neighborhood embeddings.
problem Lack of comprehensive representation of urban neighborhoods using heterogeneous data.
method Unsupervised multi-modal framework using CNN for visual features and bag-of-words for POI data.
result Urban2Vec achieves better performance than baseline models and comparable to fully-supervised methods.
TNC learns time series representations by leveraging temporal neighborhoods.
problem Complex, unlabeled time series data.
method Temporal Neighborhood Coding (TNC) with a debiased contrastive objective.
result TNC outperforms other unsupervised methods in time series clustering and classification.
This paper tackles selection bias in recommender systems by considering the neighborhood effect.
problem Selection bias in recommender systems due to filtering and user selection.
method Formalizes neighborhood effect as interference problem, introduces treatment representation, and proposes ideal loss.
result Proposed methods achieve unbiased learning when both selection bias and neighborhood effect are present.
node2vec learns node features for networks, improving prediction tasks.
problem Lack of expressive feature learning for diverse network connectivity.
method Flexible biased random walk to explore diverse neighborhoods, maximizing likelihood of preserved network neighborhoods.
result Node2vec outperforms existing techniques in multi-label classification and link prediction.
JK networks adapt to varying neighborhood sizes for better graph representation learning.
problem Fixed neighborhood aggregation limits model performance on graphs with diverse structures.
method Jumping Knowledge (JK) networks that use different neighborhood sizes for each node.
result JK networks achieve state-of-the-art performance on various graph datasets.
Learn conditional averages in PAC framework for better predictions.
problem Learning average labels over neighborhoods in unknown concept class.
method Characterization of learnability using combinatorial parameters.
result Complete characterization and sample complexity bounds.
DNA improves graph neural networks by selectively aggregating node embeddings.
problem Static neighborhood aggregation limits graph neural networks' performance.
method Dynamic neighborhood aggregation guided by attention and controlled channel connections.
result DNA outperforms current methods in transductive node classification.
Proposes a new NMF method incorporating neighborhood structure for better anomaly detection.
problem NMF's inability to incorporate neighborhood structure information limits its performance in nonlinear manifold structures.
method Integrates neighborhood structure information using Minimum Spanning Tree (MST) within NMF framework.
result Empirical results show superior performance in anomaly detection using the proposed method.
Graph Denoising Policy Network learns robust representations from noisy graphs.
problem Noise sensitivity in graph representation learning.
method Reinforcement learning to select signal neighborhoods and aggregate features.
result Significantly outperforms state-of-the-art methods on node classification tasks.
New algorithm learns kernels for multi-task learning.
problem Improving performance in multi-task learning scenarios.
method Support Vector Machine-regularized model with neighborhood kernels.
result Consistently outperforms traditional kernel learning methods.
Neighborhood sampling affects graph neural network training outcomes.
problem Understanding the impact of neighborhood sampling on graph neural network training.
method Theoretical analysis using neural tangent kernels and Gaussian processes.
result Posterior covariance differs for different neighborhood sampling approaches, indicating no dominant approach.
NEAR improves graph classification by aggregating edge information.
problem Loss of local structure and relationships in 1-hop neighborhood GNNs.
method Proposes NEAR, a framework that aggregates edge information between nodes in the neighborhood.
result NEAR improves graph classification tasks over existing 1-hop based GNN algorithms.
A new data-driven sampling method improves GraphSAGE's accuracy in node classification.
problem High variance in neighborhood sampling leads to sub-optimum accuracy in GraphSAGE.
method A data-driven node sampling approach using a non-linear regressor trained with reinforcement learning.
result Enhanced GraphSAGE accuracy in inductive node classification benchmarks.
Enhances generative models with latent features and neighborhood memories.
problem Current generative models only use one of two components: learned features or instance recall.
method Proposes methods to integrate neighborhood information into a flow model's latent space.
result Empirically shows significant improvement over baselines on image datasets.
New method learns optimal cost for machine learning models.
problem Optimizing machine learning models under distributional uncertainty.
method Data-driven approach to define distributional uncertainty neighborhoods.
result Improves upon various machine learning estimators.
GESF learns flexible graph node embeddings without specifying neighborhood or dependence.
problem Graph node embedding flexibility and neighborhood dependence specification.
method GESF uses set function technique to learn arbitrary representation functions from neighborhoods, automatically deciding neighbor significance.
result GESF outperforms state-of-the-art methods on graph classification tasks.
The k-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct k-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to const…
Motivated by an abstract notion of low-level edge detector filters, we propose a simple method of unsupervised feature construction based on pairwise statistics of features. In the first step, we construct neighborhoods of features by regrouping features that correlate. Then we use these subsets as filters to produce n…
EHNA learns node embeddings from historical network neighborhoods.
problem Capturing temporal information in evolving networks.
method Temporal random walk and deep learning model with attention mechanism.
result EHNA outperforms existing methods in network reconstruction and link prediction tasks.
New LNS framework improves integer program solving.
problem Solving large-scale integer linear programs efficiently.
method Large neighborhood search with imitation and reinforcement learning.
result Framework significantly outperforms commercial solvers.
We derive spectral sequences for the intersection homology of stratified fibrations and approximate tubular neighborhoods in manifold stratified spaces. These neighborhoods include regular neighborhoods in PL stratified spaces.
New algorithm learns Markov network structures efficiently.
problem Learning Markov network structures without chordality assumptions.
method Local penalized likelihood ratio tests and two-stage hill-climbing algorithm.
result PLRHC-BIC0.5 algorithm compares favorably against state-of-the-art methods. F-GCN improves graph convolutional networks for semi-supervised node classification.
problem Improving representation capacity of graph convolutional networks for multi-hop neighborhood information.
method Proposes a mathematically motivated, yet simple extension to existing GCNs.
result F-GCN outperforms state-of-the-art models on six out of eight datasets.
Extends neighborhood regression to algebraic structures for encoding conditional independence.
problem Encoding conditional independence statements in Gaussian distributions.
method Defining a neighborhood lattice based on generalized neighborhood regression.
result Algebraic structure provides an economic encoding of all conditional independence statements.
Study geodesics entering a fixed cusp neighborhood multiple times.
problem Understanding geodesics entering a specific cusp neighborhood multiple times.
method Investigate reciprocal geodesics entering a fixed cusp neighborhood a fixed number of times.
result Characterized the class of reciprocal geodesics entering a fixed cusp neighborhood a fixed number of times.
Moves connect multibranched surfaces with same neighborhoods.
problem Connecting multibranched surfaces with identical neighborhoods.
method Introduces moves to connect multibranched surfaces.
result Any two multibranched surfaces can be connected in finitely many steps.
In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity. Typically, data is collected across a cohort of subjects and the scientific objectives consist of estimating population and subject-specific g…
Proposes TTNPE for tensor data embedding with improved trade-offs.
problem Embedding multi-dimensional tensor data into low dimensions.
method Tensor Train Neighborhood Preserving Embedding (TTNPE) with novel optimization approaches.
result Improves classification, computation, and dimensionality reduction trade-offs.
GAP learns node representations by attending to different parts of its neighborhood.
problem Context-free learning of node representations in graph representation learning.
method Graph Neighborhood Attentive Pooling (GAP) using attentive pooling networks.
result GAP outperforms 10 state-of-the-art methods on link prediction and clustering tasks.
This research improves classification performance by learning a distance metric from balanced data.
problem Data imbalance in learning methods.
method Extracts a low-dimensional manifold, learns local neighborhood relationships, and optimizes distance metric.
result The proposed method outperforms other approaches, especially in imbalanced datasets.
Proposes a measure to predict generalization in non-matching environments.
problem Characterizing and comparing generalization of machine learning models in non-matching environments.
method Neighborhood invariance measure, calculating invariance as the largest fraction of transformed points classified into the same class.
result Strong and robust correlation between neighborhood invariance and actual out-of-domain generalization.
Paper extends topic models using neighborhood aggregation for better performance.
problem Extending topic models with pre-trained word embeddings and nonlinear output functions.
method Network view of topic models, neighborhood aggregation algorithm.
result Approach outperforms state-of-the-art supervised Latent Dirichlet Allocation.
Skeleta and other pure subsets of manifold stratified spaces are shown to have neighborhoods which are teardrops of stratified approximate fibrations (under dimension and compactness assumptions). In general, the stratified approximate fibrations cannot be replaced by bundles, and the teardrops cannot be replaced by ma…
New graph kernels for evolving graphs with ordered neighborhoods.
problem Graphs with evolving edges over time.
method Combining convolutional subgraph kernels and string kernels, new scalable algorithms for generating graph feature maps.
result Neighborhood ordering yields more informative features.
New framework distinguishes knots via neighborhood invariants.
problem Distinguishing knots and knotoids.
method Study of knotoid spectra and neighborhood invariants.
result Neighborhood invariants can distinguish knots of higher Gordian distance.
GCNs improve regression tasks by aggregating neighbor signals.
problem GCNs' statistical properties in regression tasks are poorly understood.
method Examined two GCN convolutions and their impact on learning error.
result GCNs have a bias-variance trade-off that depends on neighborhood size and topology.
New method for estimating local structure around target nodes in DAGs.
problem Challenges in learning causal DAG structures in high-dimensional settings.
method Constraint-based method for estimating local structure around multiple target nodes.
result Consistency results for estimating local neighborhood structure of target nodes.
Maximally hyperbolic solutions contain future neighborhoods of intersecting hypersurfaces.
problem Maximally globally hyperbolic solutions of higher-dimensional vacuum Einstein equations.
method Analyzing intersections of characteristic hypersurfaces.
result Contains a future neighborhood of intersecting hypersurfaces.
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
problem Learning from incomplete graphs with missing node attributes.
method Introduces PaGNNs with novel partial aggregation functions for incomplete graph data.
result Demonstrates effectiveness and efficiency of PaGNNs on various datasets.
Graph DNA uses Bloom filters to efficiently encode deep graph neighborhoods for better collaborative filtering.
problem Collaborative filtering struggles with exploiting deeper graph neighborhoods due to high time and space complexity.
method Graph DNA employs Bloom filters to compute approximate deep neighborhood information in linear time, enabling efficient encoding and utilization in collaborative filtering.
result Graph DNA significantly improves collaborative filtering performance with minimal computational and memory overhead.
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.
Proposes robust local scaling using conditional quantiles of graph similarities.
problem Spectral analysis sensitivity to parameters and noise.
method Auto-encoding neural network for inferring conditional quantiles of similarity functions.
result Proposed approach outperforms existing methods in spectral clustering and single-example label propagation.
New method pools labels from similar data items to improve learning from small samples.
problem Learning from small, human-annotated samples with potential disagreement among annotators.
method Proposes neighborhood-based pooling for sharing labels across similar data items.
result Improves learning from small, noisy samples by pooling labels from similar items.