Papers
4,025 papers found
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
Hanie Sedghi, Majid Janzamin, Anima Anandkumar
Pseudo-Marginal Slice Sampling
Iain Murray, Matthew Graham
Quantization based Fast Inner Product Search
Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski et al.
Random Forest for the Contextual Bandit Problem
Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy et al.
Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments
Guillaume W. Basse, Hossein Azari Soufiani, Diane Lambert
Revealing Graph Bandits for Maximizing Local Influence
Alexandra Carpentier, Michal Valko
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA
Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu
Robust Covariate Shift Regression
Xiangli Chen, Mathew Monfort, Anqi Liu et al.
Scalable and Sound Low-Rank Tensor Learning
Hao Cheng, Yaoliang Yu, Xinhua Zhang et al.
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation
Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar
Scalable Gaussian Process Classification via Expectation Propagation
Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
William Herlands, Andrew Wilson, Hannes Nickisch et al.
Scalable geometric density estimation
Ye Wang, Antonio Canale, David Dunson
Scalable MCMC for Mixed Membership Stochastic Blockmodels
Wenzhe Li, Sungjin Ahn, Max Welling
Semi-Supervised Learning with Adaptive Spectral Transform
Hanxiao Liu, Yiming Yang
Sequential Inference for Deep Gaussian Process
Yali Wang, Marcus Brubaker, Brahim Chaib-Draa et al.
Simple and Scalable Constrained Clustering: a Generalized Spectral Method
Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla et al.
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces
Amirali Abdullah, Ravi Kumar, Andrew McGregor et al.
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Spectral M-estimation with Applications to Hidden Markov Models
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Stochastic Neural Networks with Monotonic Activation Functions
Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider et al.
Stochastic Variational Inference for the HDP-HMM
Aonan Zhang, San Gultekin, John Paisley
Streaming Kernel Principal Component Analysis
Mina Ghashami, Daniel J. Perry, Jeff Phillips