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Found 10 papers, 5 papers with code
Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems
We also devise a simulation-guided approach for training NBCs, aiming to achieve tightness in computing precise certified lower and upper bounds.
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support.
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
The posterior in probabilistic programs with stochastic support decomposes as a weighted sum of the local posterior distributions associated with each possible program path.
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors.
Nonparametric Involutive Markov Chain Monte Carlo
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL).
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming
We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds.
Nonparametric Hamiltonian Monte Carlo
A challenging goal is to develop general purpose inference algorithms that work out-of-the-box for arbitrary programs in a universal probabilistic programming language (PPL).
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently
We show that the standard computational pipeline of probabilistic programming systems (PPSs) can be inefficient for estimating expectations and introduce the concept of expectation programming to address this.
Supermartingales, Ranking Functions and Probabilistic Lambda Calculus
We introduce a method for proving almost sure termination in the context of lambda calculus with continuous random sampling and explicit recursion, based on ranking supermartingales.
Programming Languages Logic in Computer Science F.3.2
A Differential-form Pullback Programming Language for Higher-order Reverse-mode Automatic Differentiation
Building on the observation that reverse-mode automatic differentiation (AD) -- a generalisation of backpropagation -- can naturally be expressed as pullbacks of differential 1-forms, we design a simple higher-order programming language with a first-class differential operator, and present a reduction strategy which exactly simulates reverse-mode AD.
