Oregon
Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning
In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.
AlgorithmicStabilityandGeneralizationofan UnsupervisedFeatureSelectionAlgorithm
Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper,we propose an innovativeunsupervised feature selection algorithm attaining this stability with provable guarantees.
Unlocking the Potential of Global Human Expertise
For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency
Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning
In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.
Decentralized Training of Foundation Models in Heterogeneous Environments
Training foundation models, such as GPT -3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months.