Learning Graphical Models
Appendix A A Stochastic Markov Model of a 2 Server Load Balancing Problem
Similar to the proof of Proposition 12, given the stability constraint in Eq. Eq. (4), we have C 0, l Theorem 14. Multi-agent load balancing is MPG with the VBF Solid and dashed arrows represent deterministic and non-deterministic procedures respectively. Real-world network applications can be CPU-bound or IO-bound [47, 48]. The simulator allows configuring applications that require multi-stage processes switching between CPU/IO queues (Figure 1b). Two different processing models are used for CPU and IO queues, respectively.
Robust Anytime Learning of Markov Decision Processes
Markov decision processes (MDPs) are formal models commonly used in sequential decision-making.
Exploration via Planning for Information about the Optimal Trajectory
Furthermore, in continuous state-action settings these methods must make coarse approximations to be computationally tractable (e.g.
On Computing Probabilistic Explanations for Decision Trees Marcelo Arenas
Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML 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.
