18 citations · 25 across the 7 of their papers we have counts for
3 papers · 1 filter
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
Traditional multi-armed bandit (MAB) formulations usually make certain assumptions about the underlying arms' distributions, such as bounds on the support or their tail behaviour.…
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha +1
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 ro…
Constrained regret minimization for multi-criterion multi-armed bandits
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
We consider a stochastic multi-armed bandit setting and study the problem of constrained regret minimization over a given time horizon. Each arm is associated with an unknown, poss…