Summary of No-regret Is Not Enough! Bandits with General Constraints Through Adaptive Regret Minimization, by Martino Bernasconi et al.
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization
by Martino Bernasconi, Matteo Castiglioni, Andrea Celli
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper generalizes the bandits with knapsacks framework by introducing long-term resource-consumption constraints. The goal is to maximize cumulative reward while minimizing constraint violations. Conventional methods fail in some cases, but a weakly adaptive primal and dual algorithm can circumvent this issue, achieving sublinear regret and constraint violations for both stochastic and adversarial inputs. This approach provides best-of-both-worlds guarantees and improves the understanding of contextual bandits with linear constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper takes the “bandits with knapsacks” framework and adds a new layer of complexity by introducing long-term resource limits. The challenge is to balance getting rewards while not exceeding these limits. Some methods don’t work well in this scenario, but the researchers came up with an innovative solution that does. This solution works equally well when the future is certain or uncertain. It’s like having a plan A and a plan B, both of which can help you achieve your goals. |
