Papers
Trading off Rewards and Errors in Multi-Armed Bandits
Akram Erraqabi, Alessandro Lazaric, Michal Valko et al.
Random projection design for scalable implicit smoothing of randomly observed stochastic processes
Francois Belletti, Evan Sparks, Alexandre Bayen et al.
Horde of Bandits using Gaussian Markov Random Fields
Sharan Vaswani, Mark Schmidt, Laks Lakshmanan
Sparse Randomized Partition Trees for Nearest Neighbor Search
Kaushik Sinha, Omid Keivani
Learning Nonparametric Forest Graphical Models with Prior Information
Yuancheng Zhu, Zhe Liu, Siqi Sun
Data Driven Resource Allocation for Distributed Learning
Travis Dick, Mu Li, Venkata Krishna Pillutla et al.
Minimax Approach to Variable Fidelity Data Interpolation
Alexey Zaytsev, Evgeny Burnaev
High-dimensional Time Series Clustering via Cross-Predictability
Dezhi Hong, Quanquan Gu, Kamin Whitehouse
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Feras Saad, Vikash Mansinghka
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Meelis Kull, Telmo Silva Filho, Peter Flach
Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)
Miaoyan Wang, Yun Song
Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence Rates
Joon Kwon, Vianney Perchet
Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman et al.
Local Perturb-and-MAP for Structured Prediction
Gedas Bertasius, Qiang Liu, Lorenzo Torresani et al.
Exploration-Exploitation in MDPs with Options
Ronan Fruit, Alessandro Lazaric
Co-Occurring Directions Sketching for Approximate Matrix Multiply
Youssef Mroueh, Etienne Marcheret, Vaibahava Goel
Bayesian Hybrid Matrix Factorisation for Data Integration
Thomas Brouwer, Pietro Lio
Fast column generation for atomic norm regularization
Marina Vinyes, Guillaume Obozinski
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds
Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai et al.
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Aaron Klein, Stefan Falkner, Simon Bartels et al.
Contextual Bandits with Latent Confounders: An NMF Approach
Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu et al.
Decentralized Collaborative Learning of Personalized Models over Networks
Paul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi
Asymptotically exact inference in differentiable generative models
Matthew Graham, Amos Storkey
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Christian Naesseth, Francisco Ruiz, Scott Linderman et al.
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot
Prateek Jain, Chi Jin, Sham Kakade et al.