228 citations · 642 across the 22 of their papers we have counts for
24 papers
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas +2
Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study…
Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization
Matias Alvo, Daniel Russo, Yash Kanoria +1
We argue that inventory management presents unique opportunities for the reliable application of deep reinforcement learning (DRL). To enable this, we emphasize and test two comple…
An Information-Theoretic Analysis of Nonstationary Bandit Learning
Seungki Min, Daniel Russo
In nonstationary bandit learning problems, the decision-maker must continually gather information and adapt their action selection as the latent state of the environment evolves. I…
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective
Lucas Maystre, Daniel Russo, Yu Zhao
We present a novel podcast recommender system deployed at industrial scale. This system successfully optimizes personal listening journeys that unfold over months for hundreds of m…
On the Statistical Benefits of Temporal Difference Learning
David Cheikhi, Daniel Russo
Given a dataset on actions and resulting long-term rewards, a direct estimation approach fits value functions that minimize prediction error on the training data. Temporal differen…
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation
Chao Qin, Daniel Russo
We investigate experiments that are designed to select a treatment arm for population deployment. Multi-armed bandit algorithms can enhance efficiency by dynamically allocating mea…