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Creators/Authors contains: "Ding, Jiaxin"

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  1. ; ; ; ; (, Proceedings of the 42nd International Conference on Machine Learning (ICML 2025))
    Free, publicly-accessible full text available July 14, 2026
  2. ; ; ; (, IEEE International Conference on Big Data)
    Full Text Available 
  3. ; ; (, IFIP Networking Conference (IFIP Networking))
    null (Ed.)
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  4. ; ; ; ; (, IEEE/ACM 29th International Symposium on Quality of Service (IWQOS))
    null (Ed.)
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  5. ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. We propose a novel first-order policy optimization method, Interior-point Policy Optimization (IPO), which augments the objective with logarithmic barrier functions, inspired by the interior-point method. Our proposed method is easy to implement with performance guarantees and can handle general types of cumulative multiconstraint settings. We conduct extensive evaluations to compare our approach with state-of-the-art baselines. Our algorithm outperforms the baseline algorithms, in terms of reward maximization and constraint satisfaction. 
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  6. ; ; ; ; ; ; ; ; (, Mobihoc '18 Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing)
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