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Found 10 papers, 3 papers with code
Capacity Provisioning Motivated Online Non-Convex Optimization Problem with Memory and Switching Cost
An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of active servers over time.
Best Arm Identification in Bandits with Limited Precision Sampling
The pulled arm and its instantaneous reward are revealed to the learner, whose goal is to find the best arm by minimising the expected stopping time, subject to an upper bound on the error probability.
Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget
A key feature of this algorithm is that it is designed on the basis of an information theoretic lower bound for two-armed instances.
Unsupervised Crowdsourcing with Accuracy and Cost Guarantees
We consider the problem of cost-optimal utilization of a crowdsourcing platform for binary, unsupervised classification of a collection of items, given a prescribed error threshold.
Sequential Community Mode Estimation
We propose and analyse novel algorithms for this problem, and also establish information theoretic lower bounds on the probability of error under any algorithm.
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning
We consider the problem of optimal charging/discharging of a bank of heterogenous battery units, driven by stochastic electricity generation and demand processes.
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits
In this paper, we show that specialized algorithms that exploit such parametric information are prone to inconsistent learning performance when the parameter is misspecified.
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
We study regret minimization in a stochastic multi-armed bandit setting and establish a fundamental trade-off between the regret suffered under an algorithm, and its statistical robustness.
Constrained regret minimization for multi-criterion multi-armed bandits
We consider a stochastic multi-armed bandit setting and study the problem of constrained regret minimization over a given time horizon.
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
We also compare the error bounds for our distribution oblivious algorithms with those corresponding to standard non-oblivious algorithms.
