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Found 8 papers, 0 papers with code
Achieving $\tilde{O}(1/ε)$ Sample Complexity for Constrained Markov Decision Process
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.
Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictions
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.
High-dimensional Linear Bandits with Knapsacks
We study the contextual bandits with knapsack (CBwK) problem under the high-dimensional setting where the dimension of the feature is large.
Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning
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.
Degeneracy is OK: Logarithmic Regret for Network Revenue Management with Indiscrete Distributions
We study the classical Network Revenue Management (NRM) problem with accept/reject decisions and $T$ IID arrivals.
Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies
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.
Non-stationary Bandits with Knapsacks
Finally, we also extend the non-stationarity measure to the problem of online convex optimization with constraints and obtain new regret bounds accordingly.
Online Stochastic Optimization with Wasserstein Based Non-stationarity
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).
