Papers
Modular Proximal Optimization for Multidimensional Total-Variation Regularization
Alvaro Barbero, Suvrit Sra
Multivariate Bayesian Structural Time Series Model
Jinwen Qiu, S. Rao Jammalamadaka, Ning Ning
Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization
Andrei Patrascu, Ion Necoara
Normal Bandits of Unknown Means and Variances
Wesley Cowan, Junya Honda, Michael N. Katehakis
Numerical Analysis near Singularities in RBF Networks
Weili Guo, Haikun Wei, Yew-Soon Ong et al.
On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data
Rajen D. Shah, Nicolai Meinshausen
On Binary Embedding using Circulant Matrices
Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar et al.
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
Xingguo Li, Tuo Zhao, Raman Arora et al.
On Generalized Bellman Equations and Temporal-Difference Learning
Huizhen Yu, A. Rupam Mahmood, Richard S. Sutton
Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator
Yixin Fang, Jinfeng Xu, Lei Yang
On Semiparametric Exponential Family Graphical Models
Zhuoran Yang, Yang Ning, Han Liu
On the Stability of Feature Selection Algorithms
Sarah Nogueira, Konstantinos Sechidis, Gavin Brown
On Tight Bounds for the Lasso
Sara van de Geer
OpenEnsembles: A Python Resource for Ensemble Clustering
Tom Ronan, Shawn Anastasio, Zhijie Qi et al.
Optimal Bounds for Johnson-Lindenstrauss Transformations
Michael Burr, Shuhong Gao, Fiona Knoll
Optimal Quantum Sample Complexity of Learning Algorithms
Srinivasan Arunachalam, Ronald de Wolf
Parallelizing Spectrally Regularized Kernel Algorithms
Nicole Mücke, Gilles Blanchard
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification
Prateek Jain, Sham M. Kakade, Rahul Kidambi et al.
Patchwork Kriging for Large-scale Gaussian Process Regression
Chiwoo Park, Daniel Apley
Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression
Quan Zhang, Mingyuan Zhou
pomegranate: Fast and Flexible Probabilistic Modeling in Python
Jacob Schreiber
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
Junwei Lu, Mladen Kolar, Han Liu
Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model
Clint P. George, Hani Doss
Probabilistic preference learning with the Mallows rank model
Valeria Vitelli, Øystein Sørensen, Marta Crispino et al.