Summary of Learning to Explore with Lagrangians For Bandits Under Unknown Linear Constraints, by Udvas Das and Debabrota Basu
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints
by Udvas Das, Debabrota Basu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME); 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 This paper tackles the problem of pure exploration in multi-armed bandits with unknown linear constraints. The authors propose a Lagrangian relaxation of the sample complexity lower bound for pure exploration under constraints, showing how this evolves as constraints are sequentially estimated. They also develop two computationally efficient extensions of existing algorithms, LATS and LAGEX, which achieve asymptotically optimal sample complexity upper bounds up to constraint-dependent constants. The authors conduct numerical experiments with different reward distributions and constraints, demonstrating the efficiency of LAGEX and LATS compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computer science that helps us make good decisions when we don’t know what will happen next. It’s like trying to find the best way to tune a TV or make sure people are treated fairly online. The authors come up with new ways to solve this problem, which they test on different scenarios and show that their methods work really well. |
