4 citations · 6 across the 2 of their papers we have counts for
6 papers
Masked LARk: Masked Learning, Aggregation and Reporting worKflow
Joseph J. Pfeiffer, Denis Charles, Davis Gilton +3
Today, many web advertising data flows involve passive cross-site tracking of users. Enabling such a mechanism through the usage of third party tracking cookies (3PC) exposes sensi…
Thompson Sampling in Non-Episodic Restless Bandits
Young Hun Jung, Marc Abeille, Ambuj Tewari
Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging. We study learning algori…
Online Boosting for Multilabel Ranking with Top-k Feedback
Vinod Raman, Daniel T. Zhang, Young Hun Jung +1
We present online boosting algorithms for multilabel ranking with top-k feedback, where the learner only receives information about the top k items from the ranking it provides. We…
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems
Young Hun Jung, Ambuj Tewari
Restless bandit problems are instances of non-stationary multi-armed bandits. These problems have been studied well from the optimization perspective, where the goal is to efficien…
Online Multiclass Boosting with Bandit Feedback
Daniel T. Zhang, Young Hun Jung, Ambuj Tewari
We present online boosting algorithms for multiclass classification with bandit feedback, where the learner only receives feedback about the correctness of its prediction. We propo…
Fighting Contextual Bandits with Stochastic Smoothing
Young Hun Jung, Ambuj Tewari
We introduce a new stochastic smoothing perspective to study adversarial contextual bandit problems. We propose a general algorithm template that represents random perturbation bas…