27 citations · 149 across the 34 of their papers we have counts for
25 papers · 1 filter
Efficient Contextual Bandits with Uninformed Feedback Graphs
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
Bandits with feedback graphs are powerful online learning models that interpolate between the full information and classic bandit problems, capturing many real-life applications. A…
Practical Contextual Bandits with Feedback Graphs
Mengxiao Zhang, Yuheng Zhang, Olga Vrousgou +2
While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which…
Infinite Action Contextual Bandits with Reusable Data Exhaust
Mark Rucker, Yinglun Zhu, Paul Mineiro
For infinite action contextual bandits, smoothed regret and reduction to regression results in state-of-the-art online performance with computational cost independent of the action…
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…
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…
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…
Bellman-consistent Pessimism for Offline Reinforcement Learning
Tengyang Xie, Ching-An Cheng, Nan Jiang +2
The use of pessimism, when reasoning about datasets lacking exhaustive exploration has recently gained prominence in offline reinforcement learning. Despite the robustness it adds…
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…
Off-policy Confidence Sequences
Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas
We develop confidence bounds that hold uniformly over time for off-policy evaluation in the contextual bandit setting. These confidence sequences are based on recent ideas from mar…
Lessons from Contextual Bandit Learning in a Customer Support Bot
Nikos Karampatziakis, Sebastian Kochman, Jade Huang +3
In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for cust…
Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
We propose an estimator and confidence interval for computing the value of a policy from off-policy data in the contextual bandit setting. To this end we apply empirical likelihood…
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé +2
We design and study a Contextual Memory Tree (CMT), a learning memory controller that inserts new memories into an experience store of unbounded size. It is designed to efficiently…
Fast Label Embeddings for Extremely Large Output Spaces
Paul Mineiro, Nikos Karampatziakis
Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive…
Learning Reductions that Really Work
Alina Beygelzimer, Hal Daumé, John Langford +1
We provide a summary of the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of problems, and show that this appr…
Scalable Multilabel Prediction via Randomized Methods
Nikos Karampatziakis, Paul Mineiro
Modeling the dependence between outputs is a fundamental challenge in multilabel classification. In this work we show that a generic regularized nonlinearity mapping independent pr…
Fast Label Embeddings via Randomized Linear Algebra
Paul Mineiro, Nikos Karampatziakis
Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive…
Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolut…
Combining Structured and Unstructured Randomness in Large Scale PCA
Nikos Karampatziakis, Paul Mineiro
Principal Component Analysis (PCA) is a ubiquitous tool with many applications in machine learning including feature construction, subspace embedding, and outlier detection. In thi…
Discriminative Features via Generalized Eigenvectors
Nikos Karampatziakis, Paul Mineiro
Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable tec…
Loss-Proportional Subsampling for Subsequent ERM
Paul Mineiro, Nikos Karampatziakis
We propose a sampling scheme suitable for reducing a data set prior to selecting a hypothesis with minimum empirical risk. The sampling only considers a subset of the ultimate (unk…