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Non-convex Finite-Sum Optimization Via SCSG Methods
Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan
Moreover, SCSG is never worse than the state-of-the-art methods based on variance reduction and it significantly outperforms them when the target accuracy is low.
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer
Neural Information Processing Systems http://nips.cc/
Safe and Nested Subgame Solving for Imperfect-Information Games
In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames.
Online control of the false discovery rate with decaying memory
Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
Group Sparse Additive Machine
Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang
However, the previous works mainly focus on the least squares regression problem, not the classification task.
Training Deep Networks without Learning Rates Through Coin Betting
Francesco Orabona, Tatiana Tommasi
Neural Information Processing Systems http://nips.cc/
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther
From the earliest stages of childhood, humans learn to represent high-dimensional sensory input to make temporal predictions. From the visual image of a moving tennis ball, we can imagine its trajectory, and prepare ourselves in advance to catch it.
Probabilistic Rule Realization and Selection
Haizi Yu, Tianxi Li, Lav R. Varshney
Neural Information Processing Systems http://nips.cc/
Online Dynamic Programming
Holakou Rahmanian, Manfred K. K. Warmuth
Neural Information Processing Systems http://nips.cc/
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
We prove uniqueness of the barycenter of a population of GPs, as well as convergence of the metric and the barycenter of their finite-dimensional counterparts.