Neural Information Processing Systems http://nips.cc/

Adapted with edits from [1].

Harsanyi's theorem shows that when the principals have a

Deep learning constructs are powerful tools for pattern matching and prediction.

Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time.

Neural Information Processing Systems http://nips.cc/

The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the peptides (i.e., protein subsequences) present in the original sample.

Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation.

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions are frequently non-trivial to optimize. This statement is especially true when evaluating queries in parallel, where acquisition functions are routinely non-convex, high-dimensional, and intractable. We first show that acquisition functions estimated via Monte Carlo integration are consistently amenable to gradient-based optimization. Subsequently, we identify a common family of acquisition functions, including EI and UCB, whose properties not only facilitate but justify use of greedy approaches for their maximization.

In this work, we study the fully observable discrete case where the structure of the network is given.