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Found 8 papers, 0 papers with code
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
We analyze recurrent neural networks trained with gradient descent in the supervised learning setting for dynamical systems, and prove that gradient descent can achieve optimality \emph{without} massive overparameterization.
A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback
In a wide variety of applications including online advertising, contractual hiring, and wireless scheduling, the controller is constrained by a stringent budget constraint on the available resources, which are consumed in a random amount by each action, and a stochastic feasibility constraint that may impose important operational limitations on decision-making.
Continuous-Time Multi-Armed Bandits with Controlled Restarts
Time-constrained decision processes have been ubiquitous in many fundamental applications in physics, biology and computer science.
Group-Fair Online Allocation in Continuous Time
Furthermore, as a consequence of certain ethical and economic concerns, the controller may impose deadlines on the completion of each task, and require fairness across different groups in the allocation of total time budget $B$.
Budget-Constrained Bandits over General Cost and Reward Distributions
We prove a regret lower bound for this problem, and show that the proposed algorithms achieve tight problem-dependent regret bounds, which are optimal up to a universal constant factor in the case of jointly Gaussian cost and reward pairs.
Optimal Learning for Dynamic Coding in Deadline-Constrained Multi-Channel Networks
We study the problem of serving randomly arriving and delay-sensitive traffic over a multi-channel communication system with time-varying channel states and unknown statistics.
Combinatorial Multi-Objective Multi-Armed Bandit Problem
In this paper, we introduce the COmbinatorial Multi-Objective Multi-Armed Bandit (COMO-MAB) problem that captures the challenges of combinatorial and multi-objective online learning simultaneously.
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure.
