Europe
Training Deep Models Faster with Robust, Approximate Importance Sampling
Tyler B. Johnson, Carlos Guestrin
Neural Information Processing Systems http://nips.cc/
A General Theory of Equivariant CNNs on Homogeneous Spaces
Taco S. Cohen, Mario Geiger, Maurice Weiler
The theory enables a systematicclassification of all existing G-CNNs in terms of their symmetry group, base space,andfieldtype.
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Marek Petrik, Reazul Hasan Russel
Neural Information Processing Systems http://nips.cc/
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
However, PDs are point multi-sets which can not be used in machine learning algorithms for vector data.
A loss framework for calibrated anomaly detection
Neural Information Processing Systems http://nips.cc/