no code implementations • 30 Jun 2023 • Eric Pulick, Vladimir Menkov, Yonatan Mintz, Paul Kantor, Vicki Bier
Reliable real-world deployment of reinforcement learning (RL) methods requires a nuanced understanding of their strengths and weaknesses and how they compare to those of humans.
no code implementations • 20 Jul 2022 • Eric Pulick, Shubham Bharti, Yiding Chen, Vladimir Menkov, Yonatan Mintz, Paul Kantor, Vicki M. Bier
Existing benchmark environments for ML, such as board and video games, offer well-defined benchmarks for progress, but constituent tasks are often complex, and it is frequently unclear how task characteristics contribute to overall difficulty for the machine learner.
no code implementations • 24 Aug 2019 • Vladimir Menkov, Paul Kantor
The estimates themselves are given by a ``black box'' polytomous logistic regression model (PLRM), and thus can be easily generalized to the case of any arbitrary probability-generating model.