Search Results for author:
Found 9 papers, 5 papers with code
Learning Branching Heuristics for Propositional Model Counting
In addition to step count improvements, Neuro# can also achieve orders of magnitude wall-clock speedups over the vanilla solver on larger instances in some problem families, despite the runtime overhead of querying the model.
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.
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.
Augment with Care: Contrastive Learning for Combinatorial Problems
While typical graph contrastive pre-training uses label-agnostic augmentations, our key insight is that many combinatorial problems have well-studied invariances, which allow for the design of label-preserving augmentations.
Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks
Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi.
Learning to Follow Instructions in Text-Based Games
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language.
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions.
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations
Although the state-of-the-art GPT-4 is unable to "reason" perfectly within non-language domains such as the 1D-ARC or a simple ARC subset, our study reveals that the use of object-based representations can significantly improve its reasoning ability.
Fast Matrix Multiplication Without Tears: A Constraint Programming Approach
In this work, we propose a simple yet novel Constraint Programming approach to find non-commutative algorithms for fast matrix multiplication or provide proof of infeasibility otherwise.
