2 citations · 2 across the 3 of their papers we have counts for
4 papers
Learning to Optimize through Solver-Grounded Self-Play
Xia Jiang, Yaoxin Wu, Chenyu Zhou +3
Optimization modeling is central to many decision-making scenarios, but traditionally requires extensive domain expertise. While Large Language Models (LLMs) have shown promise in…
Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks
Minshuo Li, Yaoxin Wu, Pavel Troubil +2
Complex real-world optimization problems often involve both discrete decisions and nonlinear relationships between variables. Many such problems can be modeled as polynomial-object…
Revisiting Expected Possession Value in Football: Introducing a Benchmark, U-Net Architecture, and Reward and Risk for Passes
Thijs Overmeer, Tim Janssen, Wim P. M. Nuijten
This paper introduces the first Expected Possession Value (EPV) benchmark and a new and improved EPV model for football. Through the introduction of the OJN-Pass-EPV benchmark, we…
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Igor G. Smit, Yaoxin Wu, Pavel Troubil +2
Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has bee…