Data-Driven Synthesis of Full Probabilistic Programs
نویسندگان
چکیده
Probabilistic programming languages (PPLs) provide users a clean syntax for concisely representing probabilistic processes and easy access to sophisticated built-in inference algorithms. Unfortunately, writing a PPL program by hand can be difficult for non-experts, requiring extensive knowledge of statistics and deep insights into the data. To make the modeling process easier, we have created a tool that synthesizes PPL programs from relational datasets. Our synthesizer leverages the input data to generate a program sketch, then applies simulated annealing to complete the sketch. We introduce a data-guided approach to the program mutation stage of simulated annealing; this innovation allows our tool to scale to synthesizing complete probabilistic programs from scratch. We find that our synthesizer produces accurate programs from 10,000-row datasets in 21 seconds on average.
منابع مشابه
Data-Driven Approaches to Improve the Quality of Clinical Processes: A Systematic Review
Background: Considering the emergence of electronic health records and their related technologies, an increasing attention is paid to data driven approaches like machine learning, data mining, and process mining. The aim of this paper was to identify and classify these approaches to enhance the quality of clinical processes. Methods: In order to determine the knowledge related to the research ...
متن کاملOn Repair with Probabilistic Attribute Grammars
Program synthesis and repair have emerged as an exciting area of research, driven by the potential for revolutionary advances in programmer productivity. Among most promising ideas emerging for synthesis are syntax-driven search, probabilistic models of code, and the use of input-output examples. Our paper shows how to combine these techniques and use them for program repair, which is among the...
متن کاملLearning Probabilistic Programs Using Backpropagation
Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods. However, learning probabilistic models is difficult and has not achieved the level of performance of methods such as deep neural networks on many tasks. In this paper, we attempt to address this issue by presenting a method...
متن کاملData-driven Modeling and Synthesis of Acoustical Instruments
We present a framework for the analysis and synthesis of acoustical instruments based on data driven probabilistic inference modeling Audio time series and boundary conditions of a played instrument are recorded and the non linear mapping from the control data into the audio space is inferred using the general inference framework of Cluster Weighted Modeling The resulting model is used for real...
متن کاملSupplement for: Unsupervised Learning by Program Synthesis
Unsupervised program synthesis is a domain-general framework for defining domain-specific program synthesis systems. For each domain, we expect the user to sketch a space of program hypotheses. For example, in a domain of regression problems the space of programs might include piecewise polynomials, and in a domain of visual concepts the space of programs might include graphics primitives. As p...
متن کامل