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Found 8 papers, 1 papers with code
Geometric Algorithms for $k$-NN Poisoning
We propose a label poisoning attack on geometric data sets against $k$-nearest neighbor classification.
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs
This leads to a mixed algorithmic-ML framework, which we call NN-Baker that has the capacity to approximately solve a family of graph optimization problems (e. g, maximum independent set and minimum vertex cover) in time linear to input graph size, and only polynomial to approximation parameter.
Grouping Words with Semantic Diversity
Deep Learning-based NLP systems can be sensitive to unseen tokens and hard to learn with high-dimensional inputs, which critically hinder learning generalization.
Learning Lines with Ordinal Constraints
We study the problem of finding a mapping $f$ from a set of points into the real line, under ordinal triple constraints.
Learning Mahalanobis Metric Spaces via Geometric Approximation Algorithms
Learning Mahalanobis metric spaces is an important problem that has found numerous applications.
Robust Mahalanobis Metric Learning via Geometric Approximation Algorithms
Learning Mahalanobis metric spaces is an important problem that has found numerous applications.
Algorithms for metric learning via contrastive embeddings
We study the problem of supervised learning a metric space under discriminative constraints.
Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities
Prior to this work, no finite sample upper bound was known for this estimator in more than $3$ dimensions.
