activity
20102025
most citedAbstracting Definitional Interpreters

48 citations · 122 across the 17 of their papers we have counts for

collaborators

18 papers

cs.PL2025

Designing Walrus: Relational Programming with Rich Types, On-Demand Laziness, and Structured Traces

Santiago Cuéllar, Naomi Spargo, Jonathan Daugherty +1

We present Walrus, a functional relational programming language embedded in Haskell that extends the miniKanren model with type-polymorphic unification, on-demand laziness, and a r…

cs.PL2025

The CoCompiler: DSL Lifting via Relational Compilation

Naomi Spargo, Santiago Cuéllar, Jonathan Daugherty +2

Lifting low-level or legacy code into a domain-specific language (DSL) improves our ability to understand it, enables deeper formal reasoning, and facilitates safe modification. We…

cs.PL2024★ 9 cited

KATch: A Fast Symbolic Verifier for NetKAT

Mark Moeller, Jules Jacobs, Olivier Savary Belanger +5

We develop new data structures and algorithms for checking verification queries in NetKAT, a domain-specific language for specifying the behavior of network data planes. Our result…

stat.AP2023★ 1 cited

A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census

John M. Abowd, Tamara Adams, Robert Ashmead +12

We show that individual, confidential microdata records from the 2010 U.S. Census of Population and Housing can be accurately reconstructed from the published tabular summaries. Ni…

cs.CR2023★ 6 cited

Symphony: Expressive Secure Multiparty Computation with Coordination

Ian Sweet, David Darais, David Heath +3

Context: Secure Multiparty Computation (MPC) refers to a family of cryptographic techniques where mutually untrusting parties may compute functions of their private inputs while re…

cs.LG2022★ 3 cited

Backpropagation Clipping for Deep Learning with Differential Privacy

Timothy Stevens, Ivoline C. Ngong, David Darais +3

We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each tr…