Though LLMs are capable of generating plausible programs, it's challenging to interact with the LLMs further to revise the program, especially if the user's specific requirements are different from the initial proposal.

For what purpose was the dataset created?

Effective resistances have a broad range of algorithmic implications.

Ironically, previous works neglect multi-round interaction, which is indispensable in human communication.

The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kernel.

In what follows, we will focus on this regime and assume that d > n.

In this document we provide additional details and experiments.

How can one publish a dataset with sensitive attributes in a way that both preserves privacy and enables joins with other datasets on those same sensitive attributes? This problem arises in many contexts, e.g., a hospital and an airline may want to jointly determine whether people who take long-haul flights are more likely to catch respiratory infections. If they join their data by a common keyed user identifier such as email address, they can determine the answer, though it breaks privacy. This paper shows how the hospital can generate a private sketch and how the airline can privately join with the hospital's sketch by email address. The proposed solution satisfies pure differential privacy and gives approximate answers to linear queries and optimization problems over those joins. Whereas prior work such as secure function evaluation requires sender/receiver interaction, a distinguishing characteristic of the proposed approach is that it is non-interactive. Consequently, the sketch can be published to a repository for any organization to join with, facilitating data discovery. The accuracy of the method is demonstrated through both theoretical analysis and extensive empirical evidence.