Research map / Bandits and online learning / Online convex optimization and games
Boosting for Online Convex Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Boost decision stumps with multiple rounds of weak learners | BoOCO · California Housing, Boston, and Diabetes regression datasets | normalized loss | Helped |
| Boost ridge regression with multiple rounds of weak learners | BoOCO · California Housing, Boston, and Diabetes regression datasets | normalized loss | Mixed |
Closest papers by meaning
- Online Agnostic Boosting via Regret Minimization (NeurIPS 2020 · similarity 0.92)
- Optimal Algorithms for Online Convex Optimization with Adversarial Constraints (NeurIPS 2024 · similarity 0.90)
- Beyond $\tilde{O}(\sqrt{T})$ Constraint Violation for Online Convex Optimization with Adversarial Constraints (NeurIPS 2025 · similarity 0.89)
- A Reduction from Delayed to Immediate Feedback for Online Convex Optimization with Improved Guarantees (NeurIPS 2026 · similarity 0.89)
- Capacity-Constrained Online Convex Optimization with Delayed Feedback (NeurIPS 2026 · similarity 0.89)
- Revisiting Projection-Free Online Learning with Time-Varying Constraints (AAAI 2025 · similarity 0.89)
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