Optimization
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David Woodruff
We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices.
Contextual Reserve Price Optimization in Auctions via Mixed-Integer Programming
Moreover, we show that this MIP formulation is ideal (i.e. the strongest
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White
A data-driven approach, however, could overcome limitations in such rule-based approaches and improve precision mappings for individuals.
Online Influence Maximization under Linear Threshold Model Shuai Li1 Fang Kong 1 Kejie T ang 1 Qizhi Li1
Most previous studies focus on the independent cascade (IC) model under the edge-level feedback.
Online Influence Maximization under Linear Threshold Model
Most previous studies focus on the independent cascade (IC) model under the edge-level feedback.
Linear Stochastic Bandits Under Safety Constraints
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
Neural Information Processing Systems http://nips.cc/
Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter
Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods.