scalable learning

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