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Found 4 papers, 1 papers with code
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem
We find that label-preserving augmentations are critical for the success of contrastive pre-training.
Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework
In Mixed Integer Linear Programming (MIP), a (strong) backdoor is a "small" subset of an instance's integer variables with the following property: in a branch-and-bound procedure, the instance can be solved to global optimality by branching only on the variables in the backdoor.
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments.
Learning Branching Heuristics for Propositional Model Counting
Propositional model counting or #SAT is the problem of computing the number of satisfying assignments of a Boolean formula and many discrete probabilistic inference problems can be translated into a model counting problem to be solved by #SAT solvers.
