18 citations · 21 across the 2 of their papers we have counts for
3 papers
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…
Please come back later: Benefiting from deferrals in service systems
Anmol Kagrecha, Jayakrishnan Nair
The performance evaluation of loss service systems, where customers who cannot be served upon arrival get dropped, has a long history going back to the classical Erlang B model. In…
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
Classical multi-armed bandit problems use the expected value of an arm as a metric to evaluate its goodness. However, the expected value is a risk-neutral metric. In many applicati…