Venture: a higher-order probabilistic programming platform with programmable inference (1404.0099v1)

Abstract: We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp. Unlike Church, Venture also provides a compositional language for custom inference strategies built out of scalable exact and approximate techniques. We also describe four key aspects of Venture's implementation that build on ideas from probabilistic graphical models. First, we describe the stochastic procedure interface (SPI) that specifies and encapsulates primitive random variables. The SPI supports custom control flow, higher-order probabilistic procedures, partially exchangeable sequences and ``likelihood-free'' stochastic simulators. It also supports external models that do inference over latent variables hidden from Venture. Second, we describe probabilistic execution traces (PETs), which represent execution histories of Venture programs. PETs capture conditional dependencies, existential dependencies and exchangeable coupling. Third, we describe partitions of execution histories called scaffolds that factor global inference problems into coherent sub-problems. Finally, we describe a family of stochastic regeneration algorithms for efficiently modifying PET fragments contained within scaffolds. Stochastic regeneration linear runtime scaling in cases where many previous approaches scaled quadratically. We show how to use stochastic regeneration and the SPI to implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals based on particle Markov chain Monte Carlo and mean-field variational inference techniques.

• The paper presents Venture, a higher-order probabilistic programming platform integrating a Turing-complete Lisp-like language with highly customizable inference.
• Venture introduces key innovations like the Stochastic Procedure Interface (SPI), Probabilistic Execution Traces (PETs), and Scaffolds to manage complex dependencies and facilitate local inference.
• The platform enables efficient, scalable inference techniques for diverse models, with significant implications for developing advanced AI systems that can adapt inference strategies.

Essay on "Venture: A Higher-Order Probabilistic Programming Platform with Programmable Inference"

The paper presents Venture, a sophisticated probabilistic programming platform that significantly enhances expressiveness, extensibility, and efficiency for general-purpose applications. This is achieved through the seamless integration of a Turing-complete, higher-order probabilistic language derived from Lisp, combined with a high degree of customizability in inference strategies, surpassing predecessors such as Church. Herein, we explore the core contributions, implementation details, numerical insights, and implications for future artificial intelligence research.

Core Contributions

Venture introduces four pivotal innovations that advance the state of probabilistic programming:

1. Stochastic Procedure Interface (SPI): This interface facilitates the encapsulation of primitive random variables and supports complex control flow, higher-order probabilistic procedures, and interoperability with external models. Notably, it handles partially exchangeable sequences and likelihood-free stochastic simulators, which are imperative for models that involve intricate dependencies or cannot be constructed with tractable likelihoods.
2. Probabilistic Execution Traces (PETs): These execution traces extend Bayesian networks by capturing both conditional and existential dependencies, as well as exchangeable coupling. PETs serve as a data structure to efficiently manage the execution history of probabilistic models within Venture.
3. Scaffolds: PETs are partitioned into scaffolds, which facilitate the efficient solution of local inference problems by segmenting global inference challenges into smaller, coherent sub-problems. This structured approach enables better management of computational resources and scalability.
4. Stochastic Regeneration Algorithms: These algorithms allow efficient modification of PET segments within scaffolds and ensure preservation of conditional independence. By focusing on inference consistent with existing PET structures, Venture maintains efficient runtime performance, scaling linearly in scenarios where quadratic scaling hindered previous approaches.

Implementation and Numerical Insights

The rich and varied implementation details of Venture, built on ideas borrowed and extended from probabilistic graphical models, allow for robust inference mechanisms. For instance, Venture can implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals—integrating both particle Markov chain Monte Carlo and mean-field variational inference techniques. This breadth of capability represents a significant expansion in the versatility of probabilistic programming, as demonstrated through examples like hidden Markov models and hierarchical Bayesian nonparametric models within the text.

Moreover, the capability to delineate scopes and blocks for dynamic inference customization enables graceful handling of complex probabilistic constructs such as higher-order stochastic procedures and exchangeably coupled applications that were previously cumbersome to address. This not only improves scalability but also empowers inquiry across vast and varied datasets, a property zealously sought in the domain of machine intelligence research.

Practical and Theoretical Implications

The developments presented have several profound implications for both the theoretical landscape and practical deployments in artificial intelligence. Practically, the programmability of inference opens doors to nuanced model tuning and optimization, enabling application-specific tailoring of probabilistic programs that span a variety of domains from cognitive modeling to advanced data analytic techniques. Future workloads in AI, particularly those involving large-scale, data-driven inferences, stand to benefit significantly from the enhanced tractability and execution efficiency that Venture provides.

Theoretically, Venture suggests a robust direction for future research into the formulation of comprehensive probabilistic programming frameworks, laying the foundational groundwork for crafting intelligent systems that can dynamically learn and adapt inference strategies to optimize resource utilization and accuracy outcomes. This could lead to novel developments in machine learning methodologies, as well as in the ecosystems of languages that support probabilistic AI development.

Conclusion

The introduction of Venture represents a substantial leap in probabilistic programming, embodying an abstract, flexible, and powerful platform suited for a wide array of complex, data-intensive applications. As research progresses, key opportunities for optimization and further expansion of Venture's inference capabilities could pave the way to increasingly sophisticated AI systems, capable of harnessing the full breadth of probabilistic reasoning and cognitive modeling. The paper lays a comprehensive roadmap for future exploration and development in probabilistic computing, showcasing a convergence of theoretical insight and practical demand.