17 citations · 67 across the 16 of their papers we have counts for
7 papers · 1 filter
Learning Vehicle Routing Problems using Policy Optimisation
Nasrin Sultana, Jeffrey Chan, A. K. Qin +1
Deep reinforcement learning (DRL) has been used to learn effective heuristics for solving complex combinatorial optimisation problem via policy networks and have demonstrated promi…
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
Ali Ugur Guler, Emir Demirovic, Jeffrey Chan +3
The predict+optimize problem combines machine learning ofproblem coefficients with a combinatorial optimization prob-lem that uses the predicted coefficients. While this problemcan…
Learning to Optimise General TSP Instances
Nasrin Sultana, Jeffrey Chan, A. K. Qin +1
The Travelling Salesman Problem (TSP) is a classical combinatorial optimisation problem. Deep learning has been successfully extended to meta-learning, where previous solving effor…
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
Manpreet Kaur, Flora D. Salim, Yongli Ren +3
This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the m…
Less is More: Rejecting Unreliable Reviews for Product Question Answering
Shiwei Zhang, Xiuzhen Zhang, Jey Han Lau +2
Promptly and accurately answering questions on products is important for e-commerce applications. Manually answering product questions (e.g. on community question answering platfor…
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Emir Demirović, Anna Lukina, Emmanuel Hebrard +5
Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally us…