December 14, 2016
MIA Meeting: https://youtu.be/OhvcXiP9nNU?t=2538
Daniel Huang
Harvard SEAS
Compiling probabilistic programs
Abstract: Deriving and implementing an inference algorithm for a probabilistic model can be a difficult and error-prone task. Alternatively, in probabilistic programming, a compiler is used to transform a model into an inference algorithm. In this talk, we'll present probabilistic programming from the perspective of a compiler writer. A compiler for a traditional language uses intermediate languages (ILs) and static analysis to generate efficient code. We'll highlight how these ideas can be used in probabilistic programming for generating flexible and scalable inference algorithms.
Daniel King
Hail Team, Neale Lab
Primer: What is a compiler?
Abstract: A compiler is an algorithm that transforms a source language into a target language. The transformation typically includes an optimizing pass which reduces memory or time requirements. Classic compilers transform languages such as C or Java into near-machine code such as x86 Assembly or JVM Bytecode. Recent work on Domain Specific Languages (DSLs) expands the notion of "source language" in order to enable everyone to build easy-to-reason-about abstractions without the performance penalty. In this context, I will discuss compiler design and implementation techniques with examples.
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