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Creators/Authors contains: "Panigrahi, D"

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  1. ; ; (, Proceedings of the 57th Annual ACM Symposium on Theory of Computing, STOC 2025)
    Free, publicly-accessible full text available June 30, 2026
  2. ; ; ; ; (, 38th Conference on Neural Information Processing Systems (NeurIPS 2024))
    Free, publicly-accessible full text available December 1, 2025
  3. ; ; ; ; ; ; (, International Symposium on Theoretical Aspects of Computer Science)
  4. ; ; ; (, The Thirty-ninth International Conference on Machine Learning (ICML 2022))
    A popular line of recent research incorporates ML advice in the design of online algorithms to improve their performance in typical instances. These papers treat the ML algorithm as a blackbox, and redesign online algorithms to take advantage of ML predictions. In this paper, we ask the complementary question: can we redesign ML algorithms to provide better predictions for online algorithms? We explore this question in the context of the classic rent-or-buy problem, and show that incorporating optimization benchmarks directly in ML loss functions leads to significantly better performance, while maintaining a worst-case adversarial result when the advice is completely wrong. We support this finding both through theoretical bounds and numerical simulations, and posit that “learning for optimization” is a fertile area for future research. 
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    Accepted Manuscript Full Text Available