27 citations · 149 across the 34 of their papers we have counts for
9 papers · 1 filter
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…
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
Jessica Maghakian, Paul Mineiro, Kishan Panaganti +3
In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions. Due to the scarcity of explic…
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…
Conditionally Risk-Averse Contextual Bandits
Mónika Farsang, Paul Mineiro, Wangda Zhang
Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average p…
Anytime-valid off-policy inference for contextual bandits
Ian Waudby-Smith, Lili Wu, Aaditya Ramdas +2
Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning algorithms that adaptively…
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
Yinglun Zhu, Paul Mineiro
Designing efficient general-purpose contextual bandit algorithms that work with large -- or even continuous -- action spaces would facilitate application to important scenarios suc…
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan J. Foster, John Langford +1
A central problem in sequential decision making is to develop algorithms that are practical and computationally efficient, yet support the use of flexible, general-purpose models.…
Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J. Foster +5
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its p…