Online Covering with Multiple Experts
Abstract: Designing online algorithms with machine learning predictions is a recent technique beyond the worst-case paradigm for various practically relevant online problems (scheduling, caching, clustering, ski rental, etc.). While most previous learning-augmented algorithm approaches focus on integrating the predictions of a single oracle, we study the design of online algorithms with \emph{multiple} experts. To go beyond the popular benchmark of a static best expert in hindsight, we propose a new \emph{dynamic} benchmark (linear combinations of predictions that change over time). We present a competitive algorithm in the new dynamic benchmark with a performance guarantee of $O(\log K)$, where $K$ is the number of experts, for $0-1$ online optimization problems. Furthermore, our multiple-expert approach provides a new perspective on how to combine in an online manner several online algorithms - a long-standing central subject in the online algorithm research community.
- Online facility location with multiple advice. In Advances in Neural Information Processing Systems, volume 34, pages 4661–4673, 2021.
- Online algorithms with multiple predictions. In Proc. 39th International Conference on Machine Learning, 2022.
- Online Computation with Untrusted Advice. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020), volume 151, pages 52:1–52:15, 2020.
- Online metric algorithms with untrusted predictions. In International Conference on Machine Learning, pages 345–355, 2020.
- Mixing predictions for online metric algorithms, 2023. arXiv:2304.01781.
- On-line choice of on-line algorithms. In Symposium of Discrete Algorithms (SODA), pages 432–440, 1993.
- The primal-dual method for learning augmented algorithms. In Advances in Neural Information Processing Systems, volume 33, pages 20083–20094, 2020.
- On-line learning and the metrical task system problem. Machine Learning, 39(1):35–58, 2000.
- Metrical task systems on trees via mirror descent and unfair gluing. SIAM Journal on Computing, 50(3):909–923, 2021.
- K-server via multiscale entropic regularization. In Proc. 50th Symposium on Theory of Computing, pages 3–16, 2018.
- Competitive analysis via regularization. In Proc. 25th Symposium on Discrete Algorithms, pages 436–444, 2014.
- k-servers with a smile: Online algorithms via projections. In Proc. 30th Symposium on Discrete Algorithms, pages 98–116, 2019.
- Algorithms with prediction portfolios. In Advances in Neural Information Processing Systems, 2022.
- Online algorithms for rent-or-buy with expert advice. In International Conference on Machine Learning, pages 2319–2327, 2019.
- Online algorithms for rent-or-buy with expert advice. In Proceedings of the 36th International Conference on Machine Learning, 2019.
- Learning-based frequency estimation algorithms. In Proc. Conference on Learning Representations, 2019.
- Primal-dual algorithms with predictions for online bounded allocation and ad-auctions problems. In International Conference on Algorithmic Learning Theory, pages 891–908, 2023.
- The case for learned index structures. In Proc. Conference on Management of Data, pages 489–504, 2018.
- Improving online algorithms via ML predictions. In Proc. 32nd Conference on Neural Information Processing Systems, pages 9684–9693, 2018.
- Online scheduling via learned weights. In Proc. Symposium on Discrete Algorithms, pages 1859–1877, 2020.
- Competitive caching with machine learned advice. In International Conference on Machine Learning, pages 3296–3305, 2018.
- Michael Mitzenmacher. A model for learned bloom filters, and optimizing by sandwiching. In Proc. Conference on Neural Information Processing Systems, pages 464–473, 2018.
- Michael Mitzenmacher. Scheduling with predictions and the price of misprediction. In Proc. 11th Innovations in Theoretical Computer Science Conference, 2020.
- Beyond the Worst-Case Analysis of Algorithms, chapter Algorithms with Predictions. Cambridge University Press, 2020.
- Dhruv Rohatgi. Near-optimal bounds for online caching with machine learned advice. In Proc. Symposium on Discrete Algorithms, pages 1834–1845, 2020.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
