140 citations · 1.3k across the 125 of their papers we have counts for
6 papers · 1 filter
DUM: Diversity-Weighted Utility Maximization for Recommendations
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky +1
The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the reco…
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits
Branislav Kveton, Zheng Wen, Azin Ashkan +1
A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to constraints, and then observes…
Managing your Private and Public Data: Bringing down Inference Attacks against your Privacy
Salman Salamatian, Amy Zhang, Flavio du Pin Calmon +5
We propose a practical methodology to protect a user's private data, when he wishes to publicly release data that is correlated with his private data, in the hope of getting some u…
Efficient Learning in Large-Scale Combinatorial Semi-Bandits
Zheng Wen, Branislav Kveton, Azin Ashkan
A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to combinatorial constraints, and…
Learning to Act Greedily: Polymatroid Semi-Bandits
Branislav Kveton, Zheng Wen, Azin Ashkan +1
Many important optimization problems, such as the minimum spanning tree and minimum-cost flow, can be solved optimally by a greedy method. In this work, we study a learning variant…
Matroid Bandits: Fast Combinatorial Optimization with Learning
Branislav Kveton, Zheng Wen, Azin Ashkan +2
A matroid is a notion of independence in combinatorial optimization which is closely related to computational efficiency. In particular, it is well known that the maximum of a cons…