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Loss Decomposition for Fast Learning in Large Output Spaces., , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 5626-5635. PMLR, (2018)Latent Feature Lasso., , , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 3949-3957. PMLR, (2017)Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing., , , , and . WWW, page 1318-1328. ACM, (2015)S4: a High-sparsity, High-performance AI Accelerator., , and . CoRR, (2022)Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding., , , , , , , and . KDD, page 1418-1428. ACM, (2019)Word Mover's Embedding: From Word2Vec to Document Embedding., , , , , , , and . EMNLP, page 4524-4534. Association for Computational Linguistics, (2018)Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings., , , and . NIPS, page 1008-1016. (2014)Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators., , , and . NIPS, page 2375-2383. (2014)Learning Tensor Latent Features., , , , and . CoRR, (2018)A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models., , , , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 2418-2426. JMLR.org, (2015)