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Found 4 papers, 3 papers with code
Rethinking and Benchmarking Predict-then-Optimize Paradigm for Combinatorial Optimization Problems
Numerous web applications rely on solving combinatorial optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks.
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility.
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros.
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data.
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