3 papers
cs.LG2026
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Sam Urmian, Qinyi Liu, Mohammad Khalil
We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy guarantee passes through se…
cs.DS2026
State Canonization and Early Pruning in Width-Based Automated Theorem Proving
Mateus de Oliveira Oliveira, Sam Urmian
Width-based automated theorem proving is a framework where counterexamples to graph-theoretic conjectures are searched width-wise relative to some graph width measure, such as tree…
cs.DS2026
TreeWidzard: An Engine for Width-Based Dynamic Programming and Automated Theorem Proving
Mateus de Oliveira Oliveria, Sam Urmian
In this work, we introduce TreeWidzard, an engine for developing dynamic programming algorithms that decide graph-theoretic properties parameterized by treewidth and pathwidth. Bes…