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.

[Q]: Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm’s best performance.
[A]: Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments


[Q]: Conditional probabilities are a core concept in machine learning. For ex-<lb>ample, optimal prediction of a label Y given an inputX corresponds to maximizing<lb>the conditional probability of Y given X . A common approach to inference tasks<lb>is learning a model of conditional probabilities. However, these models are often<lb>based on strong assumptions (e.g., log-linear models), and hence their estimate of<lb>conditional probabilities is not robust and is highly dependent on the validity of<lb>their assumptions.<lb>Here we propose a framework for reasoning about conditional probabilities without<lb>assuming anything about the underlying distributions, except knowledge of their<lb>second order marginals, which can be estimated from data. We show how this<lb>setting leads to guaranteed bounds on conditional probabilities, which can be calcu-<lb>lated efficiently in a variety of settings, including structured-prediction. Finally, we<lb>apply them to semi-supervised deep learning, obtaining results competitive with<lb>variational autoencoders.
[A]: Robust Conditional Probabilities


[Q]: We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trade computation time for accuracy. We show how to expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that resulting posterior inferences compare favorably to existing posterior approximation algorithms in both accuracy and efficiency.
[A]:
Variational Boosting: Iteratively Refining Posterior Approximations