2 citations · 5 across the 4 of their papers we have counts for
7 papers · 1 filter
Sound Probabilistic Inference via Guide Types
Di Wang, Jan Hoffmann, Thomas Reps
Probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize i…
Expected-Cost Analysis for Probabilistic Programs and Semantics-Level Adaption of Optional Stopping Theorems
Di Wang, Jan Hoffmann, Thomas Reps
In this article, we present a semantics-level adaption of the Optional Stopping Theorem, sketch an expected-cost analysis as its application, and survey different variants of the O…
Probabilistic Resource-Aware Session Types
Ankush Das, Di Wang, Jan Hoffmann
Session types guarantee that message-passing processes adhere to predefined communication protocols. Prior work on session types has focused on deterministic languages but many mes…
Liquid Resource Types
Tristan Knoth, Di Wang, Adam Reynolds +2
This article presents liquid resource types, a technique for automatically verifying the resource consumption of functional programs. Existing resource analysis techniques trade au…
Raising Expectations: Automating Expected Cost Analysis with Types
Di Wang, David M Kahn, Jan Hoffmann
This article presents a type-based analysis for deriving upper bounds on the expected execution cost of probabilistic programs. The analysis is naturally compositional, parametric…
Central Moment Analysis for Cost Accumulators in Probabilistic Programs
Di Wang, Jan Hoffmann, Thomas Reps
For probabilistic programs, it is usually not possible to automatically derive exact information about their properties, such as the distribution of states at a given program point…