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Found 11 papers, 2 papers with code
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference
(2) How can we computationally handle both nonlinear ODE constraints and parameter uncertainties for a generic stochastic optimization problem for resource allocation?
Bayesian regularization of empirical MDPs
Due to noise, the policy learnedfrom the estimated model is often far from the optimal policy of the underlying model.
Approximate Newton policy gradient algorithms
This paper proposes an approximate Newton method for the policy gradient algorithm with entropy regularization.
Best-Arm Identification in Correlated Multi-Armed Bandits
In this paper we consider the problem of best-arm identification in multi-armed bandits in the fixed confidence setting, where the goal is to identify, with probability $1-\delta$ for some $\delta>0$, the arm with the highest mean reward in minimum possible samples from the set of arms $\mathcal{K}$.
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round.
Integer Programming-based Error-Correcting Output Code Design for Robust Classification
We also estimate the adversarial accuracy of our ECOC-based classifiers in a white-box setting.
Multi-Armed Bandits with Correlated Arms
We consider a multi-armed bandit framework where the rewards obtained by pulling different arms are correlated.
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting
We consider a finite-armed structured bandit problem in which mean rewards of different arms are known functions of a common hidden parameter $\theta^*$.
Correlated Multi-armed Bandits with a Latent Random Source
As a result, there are regimes where our algorithm achieves a $\mathcal{O}(1)$ regret as opposed to the typical logarithmic regret scaling of multi-armed bandit algorithms.
Active Distribution Learning from Indirect Samples
At each time step, we choose one of the possible $K$ functions, $g_1, \ldots, g_K$ and observe the corresponding sample $g_i(X)$.
Request Patterns and Caching for VoD Services with Recommendation Systems
We propose a Markovian request model to capture the time-correlation in user requests and show that our model is consistent with the observations of existing empirical studies.
Networking and Internet Architecture
