no code implementations • 26 Feb 2024 • Jiashuo Jiang, Yinyu Ye
To be specific, our algorithm operates in the primal space and we resolve the primal LP for the CMDP problem at each period in an online manner, with \textit{adaptive} remaining resource capacities.
no code implementations • 2 Jan 2024 • Piao Hu, Jiashuo Jiang, Guodong Lyu, Hao Su
When the model parameters are drawn from unknown non-stationary distributions and we are given machine-learned predictions of the distributions, we develop a new algorithm from our framework with a regret $O(W_T+\sqrt{T})$, where $W_T$ measures the total inaccuracy of the machine-learned predictions.
no code implementations • 2 Nov 2023 • Wanteng Ma, Dong Xia, Jiashuo Jiang
We study the contextual bandits with knapsack (CBwK) problem under the high-dimensional setting where the dimension of the feature is large.
no code implementations • 2 Feb 2023 • Jiashuo Jiang
At each period, we take the first-stage action, observe a model parameter realization and then take the second-stage action from a feasible set that depends both on the first-stage decision and the model parameter.
no code implementations • 14 Oct 2022 • Jiashuo Jiang, Will Ma, Jiawei Zhang
We study the classical Network Revenue Management (NRM) problem with accept/reject decisions and $T$ IID arrivals.
no code implementations • 10 Jul 2022 • Boxiao Chen, Jiashuo Jiang, Jiawei Zhang, Zhengyuan Zhou
We aim to minimize the $T$-period cost, a problem that is known to be computationally intractable even under known distributions of demand and supply.
no code implementations • 25 May 2022 • Shang Liu, Jiashuo Jiang, Xiaocheng Li
Finally, we also extend the non-stationarity measure to the problem of online convex optimization with constraints and obtain new regret bounds accordingly.
no code implementations • 13 Dec 2020 • Jiashuo Jiang, Xiaocheng Li, Jiawei Zhang
We propose a unified Wasserstein-distance based measure to quantify the inaccuracy of the prior estimate in setting (i) and the non-stationarity of the system in setting (ii).