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Found 6 papers, 1 papers with code
Finite-Time Logarithmic Bayes Regret Upper Bounds
In a multi-armed bandit, we obtain $O(c_\Delta \log n)$ and $O(c_h \log^2 n)$ upper bounds for an upper confidence bound algorithm, where $c_h$ and $c_\Delta$ are constants depending on the prior distribution and the gaps of bandit instances sampled from it, respectively.
Bayesian Fixed-Budget Best-Arm Identification
We also provide a lower bound on the probability of misidentification in a $2$-armed Bayesian bandit and show that our upper bound (almost) matches it for any budget.
Contextual Pandora's Box
Pandora's Box is a fundamental stochastic optimization problem, where the decision-maker must find a good alternative while minimizing the search cost of exploring the value of each alternative.
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
We study the statistical and computational complexities of the Polyak step size gradient descent algorithm under generalized smoothness and Lojasiewicz conditions of the population loss function, namely, the limit of the empirical loss function when the sample size goes to infinity, and the stability between the gradients of the empirical and population loss functions, namely, the polynomial growth on the concentration bound between the gradients of sample and population loss functions.
Combinatorial Blocking Bandits with Stochastic Delays
Recent work has considered natural variations of the multi-armed bandit problem, where the reward distribution of each arm is a special function of the time passed since its last pulling.
Parallel Model Exploration for Tumor Treatment Simulations
The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments.
