2 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
Rethinking Knowledge Transfer in Learning Using Privileged Information
Danil Provodin, Bram van den Akker, Christina Katsimerou +2
In supervised machine learning, privileged information (PI) is information that is unavailable at inference, but is accessible during training time. Research on learning using priv…
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy
We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achi…
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achi…
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We study a posterior sampling approach to efficient exploration in constrained reinforcement learning. Alternatively to existing algorithms, we propose two simple algorithms that a…
The Impact of Batch Learning in Stochastic Bandits
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We consider a special case of bandit problems, namely batched bandits. Motivated by natural restrictions of recommender systems and e-commerce platforms, we assume that a learning…
Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream
Maurits Kaptein, Paul Ketelaar
In marketing we are often confronted with a continuous stream of responses to marketing messages. Such streaming data provide invaluable information regarding message effectiveness…