Learning Graphical Models
The staircase property: How hierarchical structure can guide deep learning
This paper identifies a structural property of data distributions that enables deep neural networks to learn hierarchically. We define the "staircase" property for
Learning Active Camera for Multi-Object Navigation Peihao Chen 1,6 Dongyu Ji1 Kunyang Lin
As a result, the agent may take a very long time to perceive the environment due to limited camera scope. In contrast, humans typically gain a larger field of view by looking around for a better perception of the environment.
Regret Bounds for Information-Directed Reinforcement Learning
As a result, IDS demonstrates impressive empirical performance [Russo and V an Roy, 2018] and outperforms UCB and TS in terms of asymptotic optimality [Kirschner et al.,
Towards Sharper Generalization Bounds for Structured Prediction Shaojie Li
This paper intends to answer the three interesting questions.
Continuously-Tempered PDMP samplers
However, these methods can struggle to sample from multi-modal or heavy-tailed distributions.
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning
Causal inference is no exception.