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Found 12 papers, 4 papers with code
Online Multiclass Boosting
Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings.
Online Boosting Algorithms for Multi-label Ranking
We consider the multi-label ranking approach to multi-label learning.
Online Learning via the Differential Privacy Lens
In this paper, we use differential privacy as a lens to examine online learning in both full and partial information settings.
Fighting Contextual Bandits with Stochastic Smoothing
We propose a general algorithm template that represents random perturbation based algorithms and identify several perturbation distributions that lead to strong regret bounds.
Online Multiclass Boosting with Bandit Feedback
We propose an unbiased estimate of the loss using a randomized prediction, allowing the model to update its weak learners with limited information.
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems
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.
Thompson Sampling in Non-Episodic Restless Bandits
Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging.
Online Boosting for Multilabel Ranking with Top-k Feedback
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.
On the Equivalence between Online and Private Learnability beyond Binary Classification
First, we show that private learnability implies online learnability in both settings.
Offline Reinforcement Learning with Resource Constrained Online Deployment
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 With Resource Constrained Online Deployment
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.
Masked LARk: Masked Learning, Aggregation and Reporting worKflow
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.
