AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

This paper studies the problem of finding graphs that maximize the number of edges, while avoiding short cycles. It formulates graph generation as a reinforcement learning task, and compares methods like AlphaZero and tabu search. A key finding is that using a curriculum - building larger graphs from good smaller graphs - significantly improves performance. The work makes progress on an open problem in extremal graph theory.

Evolutionary binary feature selection using adaptive ebola optimization search algorithm for high-dimensional datasets

Petar Veličković - CatalyzeX

State of AI Report: Compute Index V2 update!

AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

Tabu Search Algorithm - an overview

AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

PDF] Proving Theorems using Incremental Learning and Hindsight Experience Replay

AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

Adam Zsolt Wagner

Nearly 100 Mila-affiliated scientific papers accepted at NeurIPS 2023 - Mila

ASoT] Natural abstractions and AlphaZero — AI Alignment Forum

Aman Madaan (@aman_madaan) / X

AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

PDF] Proving Theorems using Incremental Learning and Hindsight Experience Replay