Research map / Bandits and online learning / Online convex optimization and games
Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Adaptive Regret of Convex and Smooth Functions (ICML 2019 · similarity 0.94)
- Small-loss Adaptive Regret for Online Convex Optimization (ICML 2024 · similarity 0.94)
- Dynamic Regret of Strongly Adaptive Methods (ICML 2018 · similarity 0.93)
- Dynamic Regret Reduces to Kernelized Static Regret (NeurIPS 2025 · similarity 0.91)
- An Equivalence Between Static and Dynamic Regret Minimization (NeurIPS 2024 · similarity 0.90)
- A Simple and Optimal Approach for Universal Online Learning with Gradient Variations (NeurIPS 2024 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.