The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by
its unnormalized (log-) probabilities. Over the past years, the machine learning community …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep
neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a …

Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …

Abstract Class-Incremental Learning (CIL)[38] trains classifiers under a strict memory
budget: in each incremental phase, learning is done for new data, most of which is …

In this work, we introduce Graph Pointer Networks (GPNs) trained using reinforcement
learning (RL) for tackling the traveling salesman problem (TSP). GPNs build upon Pointer …

We study the problem of combining neural networks with symbolic reasoning. Recently
introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as …

Preference optimization techniques have become a standard final stage for training state-of-
art large language models (LLMs). However, despite widespread adoption, the vast majority …

This paper presents an approach to learn the local-search heuristics that iteratively improves
the solution of Vehicle Routing Problem (VRP). A local-search heuristics is composed of a …

Abstract The Orienteering Problem with Time Windows (OPTW) is a combinatorial
optimization problem where the goal is to maximize the total score collected from different …