44 citations · 54 across the 5 of their papers we have counts for
Showing 2019 · stat.MLShow all
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stat.ML2019★ 2 cited
Randomized Exploration for Non-Stationary Stochastic Linear Bandits
Baekjin Kim, Ambuj Tewari
We investigate two perturbation approaches to overcome conservatism that optimism based algorithms chronically suffer from in practice. The first approach replaces optimism with a…
stat.ML2019★ 3 cited
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems
Baekjin Kim, Ambuj Tewari
We investigate the optimality of perturbation based algorithms in the stochastic and adversarial multi-armed bandit problems. For the stochastic case, we provide a unified regret a…