19 citations · 40 across the 8 of their papers we have counts for
9 papers
GOAL: A Generalist Combinatorial Optimization Agent Learner
Darko Drakulic, Sofia Michel, Jean-Marc Andreoli
Machine Learning-based heuristics have recently shown impressive performance in solving a variety of hard combinatorial optimization problems (COPs). However, they generally rely o…
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
Darko Drakulic, Sofia Michel, Florian Mai +2
Despite the success of neural-based combinatorial optimization methods for end-to-end heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we…
On the Generalization of Neural Combinatorial Optimization Heuristics
Sahil Manchanda, Sofia Michel, Darko Drakulic +1
Neural Combinatorial Optimization approaches have recently leveraged the expressiveness and flexibility of deep neural networks to learn efficient heuristics for hard Combinatorial…
Structured Time Series Prediction without Structural Prior
Darko Drakulic, Jean-Marc Andreoli
Time series prediction is a widespread and well studied problem with applications in many domains (medical, geoscience, network analysis, finance, econometry etc.). In the case of…
Simple and Effective Balance of Contrastive Losses
Arnaud Sors, Rafael Sampaio de Rezende, Sarah Ibrahimi +1
Contrastive losses have long been a key ingredient of deep metric learning and are now becoming more popular due to the success of self-supervised learning. Recent research has sho…
Distributional Reinforcement Learning for Energy-Based Sequential Models
Tetiana Parshakova, Jean-Marc Andreoli, Marc Dymetman
Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq model…