Research map / Bandits and online learning / Online convex optimization and games
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use coordinate-wise scale-invariant updates instead of a single tuned learning rate | Online linear prediction · Toy binary classification | test cross entropy | Helped |
| Use ScInOL2 rather than ScInOL1 | ScInOL algorithms · Toy binary classification | test cross entropy | Helped |
| Use ScInOL2 rather than ScInOL1 | ScInOL algorithms · Covertype, Census, Shuttle, Bank, Madelon, and MNIST | test cross entropy | Helped |
| Use ScInOL2 rather than ScInOL1 | ScInOL algorithms · Covertype, Census, Shuttle, Bank, Madelon, and MNIST | variance across runs | No clear effect |
| Use ScInOL2 or CoCoB rather than the other evaluated methods | Online classification methods · Covertype, Census, Shuttle, Bank, Madelon, and MNIST | test cross entropy | Mixed |
| Use coordinate-wise scale-invariant updates rather than learning-rate-tuned methods | Online classification methods · Covertype, Census, Shuttle, Bank, Madelon, and MNIST | variance across runs | Helped |
| Use ScInOL1's conservative potential-based updates | ScInOL1 · Toy binary classification | test cross entropy | Hurt |
| Use ScInOL2's more aggressive update policy | ScInOL2 · Online classification benchmarks | convergence speed | Helped |
Closest papers by meaning
- Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad (NeurIPS 2024 · similarity 0.86)
- Discounted Adaptive Online Learning: Towards Better Regularization (ICML 2024 · similarity 0.86)
- Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging (AAAI 2024 · similarity 0.85)
- From Average Sensitivity to Small-Loss Regret Bounds under Random-Order Model (NeurIPS 2026 · similarity 0.85)
- Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent (ICML 2025 · similarity 0.85)
- Minimax Adaptive Online Nonparametric Regression over Besov spaces (NeurIPS 2025 · similarity 0.85)
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.