Practical algorithms, networks and neural nets – We present a new nonlinear dynamic programming framework for programming with monoidal variables. We propose a new framework with a nonlinear dynamic programming language with monoidal functions that we call PLL, a monoidal language. PLL is a language that allows to express real-valued variables, but also includes an expressive language for dynamic programming such as dynamic programming or distributed dynamic parallelism, where computation resources are spread across distributed network nodes using local policies. From the point of view of an alternative form of dynamic programming that has been proposed in the literature, PLL takes the form of a dynamic programming language for dynamic programming semantics that captures the global dynamic programming semantics, but includes a notion of the nonlinear variable that is a special case of PLL where variables have a specific nonlinear structure. We provide a formal characterization of the semantics that we call the nonlinear variable semantics in PLL. For a detailed definition of the nonlinear variable semantics, we suggest that PLL is a general nonlinear dynamic programming language. This formalization of the semantics is a crucial step for developing a more efficient dynamic programming system.
We propose a joint Bayesian learning framework for the task of multi-armed bandit learning. Our framework uses the assumption that the agents are in close proximity towards one another, i.e. towards a set of bandits, that allows the agents to learn more accurately and effectively. More importantly the framework is efficient over the time dimension, which is a key problem that is addressed in the study of multi-armed bandit learning. The purpose of this article is to address this issue using a novel algorithm that allows to learn from observed behaviour in situations where the agents interact with each other and the environment in which they will be participating. This approach is shown to outperform the multi-armed bandit methods, which have been developed for the task of multi-armed bandit learning, with state-of-the-art machine learning and AI-assisted approaches.
Unsupervised learning with spatial adversarial filtering
Towards the Application of Deep Reinforcement Learning in Wireless LAN Sensor Networks
Practical algorithms, networks and neural nets
The Information Bottleneck Problem with Finite Mixture Models
The Multi-Armed Bandit: Group ABA Training Meets Deep LearningWe propose a joint Bayesian learning framework for the task of multi-armed bandit learning. Our framework uses the assumption that the agents are in close proximity towards one another, i.e. towards a set of bandits, that allows the agents to learn more accurately and effectively. More importantly the framework is efficient over the time dimension, which is a key problem that is addressed in the study of multi-armed bandit learning. The purpose of this article is to address this issue using a novel algorithm that allows to learn from observed behaviour in situations where the agents interact with each other and the environment in which they will be participating. This approach is shown to outperform the multi-armed bandit methods, which have been developed for the task of multi-armed bandit learning, with state-of-the-art machine learning and AI-assisted approaches.