Summary of Decision Making Under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets, by Charita Dellaporta and Patrick O’hara and Theodoros Damoulas
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
by Charita Dellaporta, Patrick O’Hara, Theodoros Damoulas
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed paper introduces Distributionally Robust Optimisation with Bayesian Ambiguity Sets (DRO-BAS) to address decision making under uncertainty. Bayesian inference is limited by model uncertainty or noisy observations, leading to suboptimal decisions. DRO-BAS hedges against this uncertainty by optimising the worst-case risk over a posterior-informed ambiguity set. Two sets are proposed: posterior expectations (DRO-BAS(PE)) and posterior predictives (DRO-BAS(PP)). The paper shows that both admit strong dual formulations, leading to efficient single-stage stochastic programs solvable with sample average approximation. These formulations Pareto dominate existing Bayesian DRO on the Newsvendor problem and achieve faster solve times with comparable robustness on the Portfolio problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision making under uncertainty is tricky because we don’t know how things work. This makes it hard to make good choices. Bayes’ rule helps, but sometimes this doesn’t give us the best results. To fix this, researchers propose a new way called Distributionally Robust Optimisation with Bayesian Ambiguity Sets (DRO-BAS). It’s like having a backup plan in case things don’t go as expected. They show that this method is better than what we have now and can solve problems more quickly. |
Keywords
» Artificial intelligence » Bayesian inference
