Search Results for author: Young Hun Jung

Found 11 papers, 4 papers with code

Masked LARk: Masked Learning, Aggregation and Reporting worKflow

1 code implementation27 Oct 2021 Joseph J. Pfeiffer III, Denis Charles, Davis Gilton, Young Hun Jung, Mehul Parsana, Erik Anderson

We introduce a secure multi-party compute (MPC) protocol that utilizes "helper" parties to train models, so that once data leaves the browser, no downstream system can individually construct a complete picture of the user activity.

Offline RL With Resource Constrained Online Deployment

no code implementations7 Oct 2021 Jayanth Reddy Regatti, Aniket Anand Deshmukh, Frank Cheng, Young Hun Jung, Abhishek Gupta, Urun Dogan

We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features.

Offline RL

Online Boosting for Multilabel Ranking with Top-k Feedback

no code implementations24 Oct 2019 Vinod Raman, Daniel T. Zhang, Young Hun Jung, Ambuj Tewari

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.

Thompson Sampling in Non-Episodic Restless Bandits

no code implementations12 Oct 2019 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.

Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems

1 code implementation NeurIPS 2019 Young Hun Jung, Ambuj Tewari

These problems have been studied well from the optimization perspective, where the goal is to efficiently find a near-optimal policy when system parameters are known.

Multi-Armed Bandits

Fighting Contextual Bandits with Stochastic Smoothing

no code implementations11 Oct 2018 Young Hun Jung, Ambuj Tewari

We propose a general algorithm template that represents random perturbation based algorithms and identify several perturbation distributions that lead to strong regret bounds.

Multi-Armed Bandits

Online Multiclass Boosting with Bandit Feedback

1 code implementation11 Oct 2018 Daniel T. Zhang, Young Hun Jung, Ambuj Tewari

We propose an unbiased estimate of the loss using a randomized prediction, allowing the model to update its weak learners with limited information.

General Classification

Online Learning via the Differential Privacy Lens

no code implementations NeurIPS 2019 Jacob Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari

In this paper, we use differential privacy as a lens to examine online learning in both full and partial information settings.

Multi-Armed Bandits

Online Boosting Algorithms for Multi-label Ranking

no code implementations23 Oct 2017 Young Hun Jung, Ambuj Tewari

We consider the multi-label ranking approach to multi-label learning.

Multi-Label Learning

Online Multiclass Boosting

1 code implementation NeurIPS 2017 Young Hun Jung, Jack Goetz, Ambuj Tewari

Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings.

General Classification

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