In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
--------
Question: Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of Church is defined in terms of evaluation histories and conditional distributions on such histories. Church also includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. We illustrate language features through several examples, including: a generalized Bayes net in which parameters cluster over trials, infinite PCFGs, planning by inference, and various non-parametric clustering models. Finally, we show how to implement query on any Church program, exactly and approximately, using Monte Carlo techniques.

Answer: Church: a language for generative models


Question: The deep Boltzmann machine (DBM) has been an important development in the quest for powerful “deep” probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden unit to have similar norms. We demonstrate that this regularization can be easily combined with standard stochastic maximum likelihood to yield an effective training strategy for the simultaneous training of all layers of the deep Boltzmann machine.

Answer: On Training Deep Boltzmann Machines


Question: One important challenge for a set of agents to achieve more efficient collaboration is for these agents to maintain proper models of each other. An important aspect of these models of other agents is that they are often partial and incomplete. Thus far, there are two common representations of agent models: MDP based and action based, which are both based on action modeling. In many applications, agent models may not have been given, and hence must be learnt. While it may seem convenient to use either MDP based or action based models for learning, in this paper, we introduce a new representation based on capability models, which has several unique advantages. First, we show that learning capability models can be performed efficiently online via Bayesian learning, and the learning process is robust to high degrees of incompleteness in plan execution traces (e.g., with only start and end states). While high degrees of incompleteness in plan execution traces presents learning challenges for MDP based and action based models, capability models can still learn to abstract useful information out of these traces. As a result, capability models are useful in applications in which such incompleteness is common, e.g., robot learning human model from observations and interactions. Furthermore, when used in multi-agent planning (with each agent modeled separately), capability models provide flexible abstraction of actions. The limitation, however, is that the synthesized plan is incomplete and abstract.

Answer:
Learning of Agent Capability Models with Applications in Multi-agent Planning