Papers
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob Gardner et al.
Scalable graph-based method for individual named entity identification
Sammy Khalife, Michalis Vazirgiannis
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Hongteng Xu, Dixin Luo, Lawrence Carin
Scalable High-Order Gaussian Process Regression
Shandian Zhe, Wei Xing, Robert M. Kirby
Scalable inference of topic evolution via models for latent geometric structures
Mikhail Yurochkin, Zhiwei Fan, Aritra Guha et al.
Scalable Interpretable Multi-Response Regression via SEED
Zemin Zheng, M. Taha Bahadori, Yan Liu et al.
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
Shusen Wang, Alex Gittens, Michael W. Mahoney
Scalable Knowledge Graph Construction from Text Collections
Ryan Clancy, Ihab F. Ilyas, Jimmy Lin
Scalable Learning in Reproducing Kernel Krein Spaces
Dino Oglic, Thomas Gärtner
Scalable Methods for Annotating Legal-Decision Corpora
Lisa Ferro, John Aberdeen, Karl Branting et al.
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Rob Cornish, Paul Vanetti, Alexandre Bouchard-Cote et al.
Scalable Multi Corpora Neural Language Models for ASR
Anirudh Raju, Denis Filimonov, Gautam Tiwari et al.
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong, Simon Lyddon, Chris Holmes
Scalable Place Recognition Under Appearance Change for Autonomous Driving
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin et al.
Scalable Recollections for Continual Lifelong Learning
Matthew Riemer, Tim Klinger, Djallel Bouneffouf et al.
Scalable Robust Kidney Exchange
Duncan C McElfresh, Hoda Bidkhori, John P Dickerson
Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature
Kritika Agrawal, Aakash Mittal, Vikram Pudi
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Xiang Geng, Bin Gu, Xiang Li et al.
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Cole Hurwitz, Kai Xu, Akash Srivastava et al.
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
Scalable Syntax-Aware Language Models Using Knowledge Distillation
Adhiguna Kuncoro, Chris Dyer, Laura Rimell et al.
Scalable Thompson Sampling via Optimal Transport
Ruiyi Zhang, Zheng Wen, Changyou Chen et al.
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Karren D. Yang, Caroline Uhler
Scalable Verified Training for Provably Robust Image Classification
Sven Gowal, Krishnamurthy (Dj) Dvijotham, Robert Stanforth et al.