works on

From the 1 of 7 linked papers with an AI index.

activity
20242026
collaborators

7 papers

cs.LO2026

Elton: Urn Resources for Reasoning about Adversarial Probabilistic Programs

Kwing Hei Li, Alejandro Aguirre, Philipp G. Haselwarter +2

The paper presents Elton, a higher-order separation logic for reasoning about probabilistic programs that may contain unknown adversarial code, introducing urn resources and delaye…

cs.PL2026

Modular Verification of Differential Privacy in Probabilistic Higher-Order Separation Logic (Extended Version)

Philipp G. Haselwarter, Alejandro Aguirre, Simon Oddershede Gregersen +3

Differential privacy is the standard method for privacy-preserving data analysis. The importance of having strong guarantees on the reliability of implementations of differentially…

cs.LO2025

Modular Reasoning about Error Bounds for Concurrent Probabilistic Programs (Extended Version)

Kwing Hei Li, Alejandro Aguirre, Simon Oddershede Gregersen +3

We present Coneris, the first higher-order concurrent separation logic for reasoning about error probability bounds of higher-order concurrent probabilistic programs with higher-or…

cs.LO2024

Approximate Relational Reasoning for Higher-Order Probabilistic Programs

Philipp G. Haselwarter, Kwing Hei Li, Alejandro Aguirre +3

Properties such as provable security and correctness for randomized programs are naturally expressed relationally as approximate equivalences. As a result, a number of relational p…

cs.LO2024

Error Credits: Resourceful Reasoning about Error Bounds for Higher-Order Probabilistic Programs

Alejandro Aguirre, Philipp G. Haselwarter, Markus de Medeiros +4

Probabilistic programs often trade accuracy for efficiency, and thus may, with a small probability, return an incorrect result. It is important to obtain precise bounds for the pro…

cs.LO2024

Tachis: Higher-Order Separation Logic with Credits for Expected Costs

Philipp G. Haselwarter, Kwing Hei Li, Markus de Medeiros +4

We present Tachis, a higher-order separation logic to reason about the expected cost of probabilistic programs. Inspired by the uses of time credits for reasoning about the running…