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Found 7 papers, 2 papers with code
SVM via Saddle Point Optimization: New Bounds and Distributed Algorithms
Our algorithms achieve $(1-\epsilon)$-approximations with running time $\tilde{O}(nd+n\sqrt{d / \epsilon})$ for both variants, where $n$ is the number of points and $d$ is the dimensionality.
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment
The multi-factor model is a widely used model in quantitative investment.
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns.
A Graph Attention Learning Approach to Antenna Tilt Optimization
GAQ relies on a graph attention mechanism to select relevant neighbors information, improve the agent state representation, and update the tilt control policy based on a history of observations using a Deep Q-Network (DQN).
Open World Learning Graph Convolution for Latency Estimation in Routing Networks
Accurate routing network status estimation is a key component in Software Defined Networking.
Learning Cellular Coverage from Real Network Configurations using GNNs
Cellular coverage quality estimation has been a critical task for self-organized networks.
Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors
First, we empirically find and theoretically motivate why sampling uniformly at random vastly overestimates the ranking performance of a method.
