activity
20152026
most citedMotion Planning for Unlabeled Discs with Optimality Guarantees

23 citations · 53 across the 26 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

cs.DS2026

Parity and Pattern Detection in Permutation Streams

Mark Braverman, Or Zamir

Consider a permutation of whose values arrive one at a time. We resolve two questions about the space needed to decide natural properties of such input: First, computing the…

cs.DS2026

Linear Hashing is Not That Awesome

Or Zamir

Consider the canonical universal hash family , where are chosen uniformly from , which we call linear hashing, being…

cs.DS2026

Enumerating Small Cycles

Or Stern, Or Zamir

In a seminal result of Yuster and Zwick, they showed that for any fixed , the even cycle can be detected in an -vertex graph in time . For -cycles, a folk…

cs.DS2026

Locality in Open Addressing Hash Tables

Or Zamir

Open-addressed hash tables without reordering, such as linear probing and uniform probing, are among the simplest and most widely used data structures. Their performance is traditi…

cs.DS2026

k-Coloring is Faster than Computing the Chromatic Number

Or Zamir

We prove that -coloring on -vertex graphs has a randomized algorithm running in time , where for every fixed . Previously, only the…

cs.LG2026

Proofs of Ownership for Machine Learning Models

Ran Canetti, Shafi Goldwasser, Or Zamir

With the increasing adoption of Machine Learning, protecting model ownership has become an essential challenge. We initiate a formal study of Proof of Ownership for machine learnin…

cs.CR2026

Undetectable Conversations Between AI Agents via Pseudorandom Noise-Resilient Key Exchange

Vinod Vaikuntanathan, Or Zamir

AI agents are increasingly deployed to interact with other agents on behalf of users and organizations. We ask whether two such agents, operated by different entities, can carry ou…

cs.CR2026

A Note on Non-Composability of Layerwise Approximate Verification for Neural Inference

Or Zamir

A natural and informal approach to verifiable (or zero-knowledge) ML inference over floating-point data is: ``prove that each layer was computed correctly up to tolerance ; ther…