Two new research papers submitted to arXiv explore advancements in online learning algorithms. The first paper introduces a differentially private algorithm for online prediction from experts, achieving near-optimal regret in low-loss scenarios. The second paper presents a universal algorithm for online convex optimization that adapts to various loss functions and handles delayed feedback, improving upon existing methods by adapting to curvature classes and reducing delay dependence. AI
IMPACT These papers contribute to theoretical advancements in machine learning algorithms, potentially enabling more robust and efficient systems for prediction and optimization tasks.
RANK_REASON Two academic papers on machine learning algorithms submitted to arXiv.
- arXiv
- Daniel S. Totzkay
- differential privacy
- machine learning
- Online prediction of others’ actions: the contribution of the target object, action context and movement kinematics
- Private Online Prediction from Experts with Small Losses
- Universal Online Convex Optimization With Delayed Feedback
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