20 citations · 36 across the 10 of their papers we have counts for
12 papers
Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra +4
Modern decision-making systems, from robots to web recommendation engines, are expected to adapt: to user preferences, changing circumstances or even new tasks. Yet, it is still un…
Deploying a Steered Query Optimizer in Production at Microsoft
Wangda Zhang, Matteo Interlandi, Paul Mineiro +6
Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios. As a result, it is important…
A lower confidence sequence for the changing mean of non-negative right heavy-tailed observations with bounded mean
Paul Mineiro
A confidence sequence (CS) is an anytime-valid sequential inference primitive which produces an adapted sequence of sets for a predictable parameter sequence with a time-uniform co…
Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro +1
Consider a prosthetic arm, learning to adapt to its user's control signals. We propose Interaction-Grounded Learning for this novel setting, in which a learner's goal is to interac…
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford +2
We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champ…
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning
Bogdan Mazoure, Paul Mineiro, Pavithra Srinath +3
We study session-based recommendation scenarios where we want to recommend items to users during sequential interactions to improve their long-term utility. Optimizing a long-term…