5 papers
Regularized Large Neighborhood Search
Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier +1
Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current sol…
Differentiable Knapsack and Top-k Operators via Dynamic Programming
Germain Vivier-Ardisson, Michaël E. Sander, Axel Parmentier +1
Knapsack and Top-k operators are useful for selecting discrete subsets of variables. However, their integration into neural networks is challenging as they are piecewise constant,…
Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction
Mathieu Blondel, Michael E. Sander, Germain Vivier-Ardisson +2
Autoregressive models (ARMs) currently constitute the dominant paradigm for large language models (LLMs). Energy-based models (EBMs) represent another class of models, which have h…
Learning with Local Search MCMC Layers
Germain Vivier-Ardisson, Mathieu Blondel, Axel Parmentier
Integrating combinatorial optimization layers into neural networks has recently attracted significant research interest. However, many existing approaches lack theoretical guarante…
CF-OPT: Counterfactual Explanations for Structured Prediction
Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier +1
Optimization layers in deep neural networks have enjoyed a growing popularity in structured learning, improving the state of the art on a variety of applications. Yet, these pipeli…