18 citations · 24 across the 11 of their papers we have counts for
5 papers · 1 filter
Best Arm Identification in Bandits with Limited Precision Sampling
Kota Srinivas Reddy, P. N. Karthik, Nikhil Karamchandani +1
We study best arm identification in a variant of the multi-armed bandit problem where the learner has limited precision in arm selection. The learner can only sample arms via certa…
Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget
Fathima Zarin Faizal, Jayakrishnan Nair
We consider a constrained, pure exploration, stochastic multi-armed bandit formulation under a fixed budget. Each arm is associated with an unknown, possibly multi-dimensional dist…
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning
Vivek Deulkar, Jayakrishnan Nair
We consider the problem of optimal charging/discharging of a bank of heterogenous battery units, driven by stochastic electricity generation and demand processes. The batteries in…
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…
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…