Optimization
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
Neural Information Processing Systems http://nips.cc/
Statistical Model Aggregation via Parameter Matching
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
Neural Information Processing Systems http://nips.cc/
Acceleration through Optimistic No-Regret Dynamics
Jun-Kun Wang, Jacob D. Abernethy
Zero-sum games can be solved using online learning dynamics, where a classical technique involves simulating two no-regret algorithms that play against each other and, afterT rounds, the average iterate is guaranteed to solve the original optimization problem with error decaying asO(logT/T). In this paper we show that the technique can be enhanced to a rate ofO(1/T2) by extending recent work [22, 25] that leverages optimistic learning to speed upequilibrium computation.
The Randomized Midpoint Method for Log-Concave Sampling
Neural Information Processing Systems http://nips.cc/
GraphStructuredPredictionEnergyNetworks
Specifically,GSPENs combine thecapabilities ofclassicalstructured prediction models andSPENs andhavetheability toexplicitly model localstructure whenknown or assumed, while providing the ability to learn an unknown or more global structure implicitly.
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
Sulaiman Alghunaim, Kun Yuan, Ali H. Sayed
Neural Information Processing Systems http://nips.cc/
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney
Neural Information Processing Systems http://nips.cc/
Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach
In this paper, we propose an online convex optimization approach with two different levels of adaptivity. On a higher level, our approach is agnostic to the unknown types and curvatures of the online functions, while at a lower level, it can exploit the unknown niceness of the environments and attain problem-dependent guarantees.