15 citations · 45 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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 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…