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Does Weather Matter? Causal Analysis of TV Logs
To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.
Combinatorial Cascading Bandits
The agent observes the index of the first chosen item whose weight is zero.
Cascading Bandits: Learning to Rank in the Cascade Model
We also prove gap-dependent upper bounds on the regret of these algorithms and derive a lower bound on the regret in cascading bandits.
DUM: Diversity-Weighted Utility Maximization for Recommendations
The need for diversification of recommendation lists manifests in a number of recommender systems use cases.
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits
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 stochastic weights of these items and receives their sum as a payoff.
Efficient Learning in Large-Scale Combinatorial Semi-Bandits
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 then observes stochastic weights of these items and receives their sum as a payoff.
Learning to Act Greedily: Polymatroid Semi-Bandits
Many important optimization problems, such as the minimum spanning tree and minimum-cost flow, can be solved optimally by a greedy method.
Matroid Bandits: Fast Combinatorial Optimization with Learning
The objective in these problems is to learn how to maximize a modular function on a matroid.
