Research map / Bandits and online learning / Online convex optimization and games
Selective Sampling and Imitation Learning via Online Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| selective querying of the expert (RAVIOLI) instead of passive querying (DAgger) | RAVIOLI · Cart Pole, single expert | return vs number of queries | Helped |
| small Lagrange constant alpha (e.g. alpha=1) (ablation) | RAVIOLI · Cart Pole | return | Hurt |
| large alpha values (50 to 200) (ablation) | RAVIOLI · Cart Pole | return | No clear effect |
| increase the number of experts M (1, 2, 3, 5), each individually weak, with most-confident-expert selection | RAVIOLI · Cart Pole, multiple experts | return vs queries | Mixed |
Closest papers by meaning
- Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback (NeurIPS 2026 · similarity 0.91)
- Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning (NeurIPS 2024 · similarity 0.89)
- Interactive and Hybrid Imitation Learning: Provably Beating Behavior Cloning (NeurIPS 2025 · similarity 0.89)
- Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms (ICML 2024 · similarity 0.89)
- Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift (NeurIPS 2026 · similarity 0.89)
- Reward-free World Models for Online Imitation Learning (ICML 2025 · similarity 0.88)
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