Markov Models
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.
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Dibangoye
Neural Information Processing Systems http://nips.cc/
Predictive Attractor Models
The task of sequential memory is considered challenging for models operating under biological constraints (i.e., local synaptic computations) for many reasons, including catastrophic forgetting,
On Learning Markov Chains
Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati
Neural Information Processing Systems http://nips.cc/
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
Neural Information Processing Systems http://nips.cc/
Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
While humans accomplish 72.4% of the tasks, the best