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scalable learning

24 papers

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Co-occurring keywords

kernel methods (1096) low-rank approximation (322) bayesian network (378) scalable machine learning (7) nystrom method (46) graph neural network (3945) logistic regression (382) variational inference (2069) federated learning (1320) hierarchical learning (87)

Papers

Scalable and Trustworthy Learning in Heterogeneous Networks AAAI 2025
CPa-WAC: Constellation Partitioning-based Scalable Weighted Aggregation Composition for Knowledge Graph Embedding IJCAI 2024
Scalable Learning of Item Response Theory Models AISTATS 2024
FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning AAAI 2024
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings NIPS 2023
Robust and Scalable Gaussian Process Regression and Its Applications CVPR 2023
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection RSS 2023
Hierarchical Transformer for Scalable Graph Learning IJCAI 2023
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more ICML 2022
Large-Scale Learning with Fourier Features and Tensor Decompositions NIPS 2021
Scalable Learning in Reproducing Kernel Krein Spaces ICML 2019
Exact Gaussian Processes on a Million Data Points NIPS 2019
A Degeneracy Framework for Scalable Graph Autoencoders IJCAI 2019
Learning Facial Action Units From Web Images With Scalable Weakly Supervised Clustering CVPR 2018
Local-to-Global Bayesian Network Structure Learning ICML 2017
Hierarchically Compositional Kernels for Scalable Nonparametric Learning JMLR 2017
When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks CVPR 2016
MLlib: Machine Learning in Apache Spark JMLR 2016
Scalable Learning of Bayesian Network Classifiers JMLR 2016
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks ICML 2015
Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification CVPR 2014
Scalable Kernel Methods via Doubly Stochastic Gradients NIPS 2014
A Scalable Machine Learning Approach to Go NIPS 2006
Learning Ensembles from Bites: A Scalable and Accurate Approach JMLR 2004