Bo Dai

Towards Black-box Iterative Machine Teaching

2017 · arXiv (Cornell University)

In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully …

SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition

2022 · arXiv (Cornell University)

Methods that extract policy primitives from offline demonstrations using deep generative models have shown promise at accelerating reinforcement learning(RL) for new tasks. Intuitively, these methods should also help to trainsafeRLagents because they enforce useful skills. …

Model Selection in Batch Policy Optimization

2021 · arXiv (Cornell University)

We study the problem of model selection in batch policy optimization: given a fixed, partial-feedback dataset and $M$ model classes, learn a policy with performance that is competitive with the policy derived from the best …

The Curse of Passive Data Collection in Batch Reinforcement Learning

2021 · arXiv (Cornell University)

In high stake applications, active experimentation may be considered too risky and thus data are often collected passively. While in simple cases, such as in bandits, passive and active data collection are similarly effective, the …