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Found 12 papers, 7 papers with code
Sensiverse: A dataset for ISAC study
In order to address the lack of applicable channel models for ISAC research and evaluation, we release Sensiverse, a dataset that can be used for ISAC research.
Does it pay to optimize AUC?
To better understand the value of optimizing for AUC, we present an efficient algorithm, namely AUC-opt, to find the provably optimal AUC linear classifier in $\mathbb{R}^2$, which runs in $\mathcal{O}(n_+ n_- \log (n_+ n_-))$ where $n_+$ and $n_-$ are the number of positive and negative samples respectively.
Fast Online Node Labeling for Very Large Graphs
This paper studies the online node classification problem under a transductive learning setting.
Why do embedding spaces look as they do?
The power of embedding representations is a curious phenomenon.
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
In this paper, we consider approximate Frank-Wolfe (FW) algorithms to solve convex optimization problems over graph-structured support sets where the linear minimization oracle (LMO) cannot be efficiently obtained in general.
Stochastic Hard Thresholding Algorithms for AUC Maximization
In this paper, we aim to develop stochastic hard thresholding algorithms for the important problem of AUC maximization in imbalanced classification.
Online AUC Optimization for Sparse High-Dimensional Datasets
The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is abundant.
Dual Averaging Method for Online Graph-structured Sparsity
Online learning algorithms update models via one sample per iteration, thus efficient to process large-scale datasets and useful to detect malicious events for social benefits, such as disease outbreak and traffic congestion on the fly.
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis.
A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks
As a case study, we specialize SG-Pursuit to optimize a number of well-known score functions for two typical tasks, including detection of coherent dense and anomalous connected subspace clusters in real-world networks.
Technical Report: A Generalized Matching Pursuit Approach for Graph-Structured Sparsity
Sparsity-constrained optimization is an important and challenging problem that has wide applicability in data mining, machine learning, and statistics.
Technical Report: Graph-Structured Sparse Optimization for Connected Subgraph Detection
Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy.
