140 citations · 1.3k across the 125 of their papers we have counts for
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Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani, Abbas Mehrabian, Audrey Durand +1
We propose , a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampl…
Randomized Exploration in Generalized Linear Bandits
Branislav Kveton, Manzil Zaheer, Csaba Szepesvari +3
We study two randomized algorithms for generalized linear bandits. The first, GLM-TSL, samples a generalized linear model (GLM) from the Laplace approximation to the posterior dist…
Waterfall Bandits: Learning to Sell Ads Online
Branislav Kveton, Saied Mahdian, S. Muthukrishnan +2
A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that…
Empirical Bayes Regret Minimization
Chih-Wei Hsu, Branislav Kveton, Ofer Meshi +2
Most bandit algorithm designs are purely theoretical. Therefore, they have strong regret guarantees, but also are often too conservative in practice. In this work, we pioneer the i…
Perturbed-History Exploration in Stochastic Linear Bandits
Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh +1
We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model…
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh +1
We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds i.i.d. pseudo-rewards to its history in round an…