15 citations · 45 across the 11 of their papers we have counts for
11 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…
Offline RL With Resource Constrained Online Deployment
Jayanth Reddy Regatti, Aniket Anand Deshmukh, Frank Cheng +3
Offline reinforcement learning is used to train policies in scenarios where real-time access to the environment is expensive or impossible. As a natural consequence of these harsh…
On the Equivalence between Online and Private Learnability beyond Binary Classification
Young Hun Jung, Baekjin Kim, Ambuj Tewari
Alon et al. [2019] and Bun et al. [2020] recently showed that online learnability and private PAC learnability are equivalent in binary classification. We investigate whether this…
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…
Online Learning via the Differential Privacy Lens
Jacob Abernethy, Young Hun Jung, Chansoo Lee +2
In this paper, we use differential privacy as a lens to examine online learning in both full and partial information settings. The differential privacy framework is, at heart, less…
Online Boosting Algorithms for Multi-label Ranking
Young Hun Jung, Ambuj Tewari
We consider the multi-label ranking approach to multi-label learning. Boosting is a natural method for multi-label ranking as it aggregates weak predictions through majority votes,…
Online Multiclass Boosting
Young Hun Jung, Jack Goetz, Ambuj Tewari
Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings. However, the multiclass extension is in the batch setting an…