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S. Coman
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Probabilistic programming offers an intuitive and expressive way to define statistical models, rendering it particularly effective in modeling problems where uncertainty plays a crucial role.
As adoption increases and models become more expressive, the challenge of effective inference becomes increasingly pronounced.
Effective inference often requires tailoring algorithms to the structure of the underlying model. While many probabilistic programming systems allow users to implement custom inference strategies via programmable inference, this process remains largely manual and heavily reliant on domain-specific expertise, particularly for sampling-based methods.
This paper investigates the use of Satisfiability Modulo Theories (SMT) to automate the generation of tailored, observation-aware proposals for guiding inference within Metropolis-Hastings in Gen, a probabilistic programming system. By reformulating the search for high-likelihood traces as a constraint optimization problem, this work explores whether SMT-based solutions can improve proposal quality and convergence. Empirical results indicate that SMT-derived traces offer a promising starting point for inference but are less effective as an active search heuristic. These findings suggest a new direction for automated, structure-aware proposal generation in probabilistic programming. ...
As adoption increases and models become more expressive, the challenge of effective inference becomes increasingly pronounced.
Effective inference often requires tailoring algorithms to the structure of the underlying model. While many probabilistic programming systems allow users to implement custom inference strategies via programmable inference, this process remains largely manual and heavily reliant on domain-specific expertise, particularly for sampling-based methods.
This paper investigates the use of Satisfiability Modulo Theories (SMT) to automate the generation of tailored, observation-aware proposals for guiding inference within Metropolis-Hastings in Gen, a probabilistic programming system. By reformulating the search for high-likelihood traces as a constraint optimization problem, this work explores whether SMT-based solutions can improve proposal quality and convergence. Empirical results indicate that SMT-derived traces offer a promising starting point for inference but are less effective as an active search heuristic. These findings suggest a new direction for automated, structure-aware proposal generation in probabilistic programming. ...
Probabilistic programming offers an intuitive and expressive way to define statistical models, rendering it particularly effective in modeling problems where uncertainty plays a crucial role.
As adoption increases and models become more expressive, the challenge of effective inference becomes increasingly pronounced.
Effective inference often requires tailoring algorithms to the structure of the underlying model. While many probabilistic programming systems allow users to implement custom inference strategies via programmable inference, this process remains largely manual and heavily reliant on domain-specific expertise, particularly for sampling-based methods.
This paper investigates the use of Satisfiability Modulo Theories (SMT) to automate the generation of tailored, observation-aware proposals for guiding inference within Metropolis-Hastings in Gen, a probabilistic programming system. By reformulating the search for high-likelihood traces as a constraint optimization problem, this work explores whether SMT-based solutions can improve proposal quality and convergence. Empirical results indicate that SMT-derived traces offer a promising starting point for inference but are less effective as an active search heuristic. These findings suggest a new direction for automated, structure-aware proposal generation in probabilistic programming.
As adoption increases and models become more expressive, the challenge of effective inference becomes increasingly pronounced.
Effective inference often requires tailoring algorithms to the structure of the underlying model. While many probabilistic programming systems allow users to implement custom inference strategies via programmable inference, this process remains largely manual and heavily reliant on domain-specific expertise, particularly for sampling-based methods.
This paper investigates the use of Satisfiability Modulo Theories (SMT) to automate the generation of tailored, observation-aware proposals for guiding inference within Metropolis-Hastings in Gen, a probabilistic programming system. By reformulating the search for high-likelihood traces as a constraint optimization problem, this work explores whether SMT-based solutions can improve proposal quality and convergence. Empirical results indicate that SMT-derived traces offer a promising starting point for inference but are less effective as an active search heuristic. These findings suggest a new direction for automated, structure-aware proposal generation in probabilistic programming.
Performance impact of the modular architecture in the incremental SGLR parsing algorithm
Research Project TU Delft
Bachelor thesis
(2021)
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S.M. Coman, E. Visser, J. Denkers, D.A.A. Pelsmaeker, R.R. Venkatesha Prasad
JSGLR2 is a modular Java implementation of the SGLR parsing algorithm that supports systematic benchmarking and improvement of its several parsing variants. By splitting the code into several components, they can be tested in isolation and thus optimized more effortlessly. The modular architecture, although beneficial for efficiently identifying and implementing optimizations, negatively impacts the performance of the parsing algorithm. This paper aims to measure the overhead introduced by the code architecture for one of the variants, more specifically the incremental variant, which combines incremental parsing with SGLR parsing. It does so by comparing the original implementation with a version with the modularity removed. The evaluation is done on programming languages used in practice: Java, WebDSL and SDF3. The results show that the inlined parser outperforms the previous one, achieving speedups of up to 16% in batch parsing and up to 10% in incremental parsing.
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JSGLR2 is a modular Java implementation of the SGLR parsing algorithm that supports systematic benchmarking and improvement of its several parsing variants. By splitting the code into several components, they can be tested in isolation and thus optimized more effortlessly. The modular architecture, although beneficial for efficiently identifying and implementing optimizations, negatively impacts the performance of the parsing algorithm. This paper aims to measure the overhead introduced by the code architecture for one of the variants, more specifically the incremental variant, which combines incremental parsing with SGLR parsing. It does so by comparing the original implementation with a version with the modularity removed. The evaluation is done on programming languages used in practice: Java, WebDSL and SDF3. The results show that the inlined parser outperforms the previous one, achieving speedups of up to 16% in batch parsing and up to 10% in incremental parsing.