Distributionally Robust Optimization via Ball Oracle Acceleration
Authors: Yair Carmon, Danielle Hausler
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop and analyze algorithms for distributionally robust optimization (DRO) of convex losses. In particular, we consider group-structured and bounded f-divergence uncertainty sets. Our approach relies on an accelerated method that queries a ball optimization oracle, i.e., a subroutine that minimizes the objective within a small ball around the query point. Our main contribution is efficient implementations of this oracle for DRO objectives. For DRO with N non-smooth loss functions, the resulting algorithms find an -accurate solution with first-order oracle queries to individual loss functions. Compared to existing algorithms for this problem, we improve complexity by a factor of up to 4/3. |
| Researcher Affiliation | Academia | Yair Carmon Tel Aviv University ycarmon@tauex.tau.ac.il Danielle Hausler Tel Aviv University hausler@mail.tau.ac.il |
| Pseudocode | No | While the paper refers to algorithms by number (e.g., "Algorithm 1", "Algorithm 3", "Algorithm 4"), the actual pseudocode blocks or structured algorithm listings are not present within the PDF document. |
| Open Source Code | No | The paper does not provide any statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | This is a theoretical paper focused on algorithm design and complexity analysis. It does not conduct empirical studies, and therefore, no datasets are used or mentioned for training. |
| Dataset Splits | No | This is a theoretical paper focused on algorithm design and complexity analysis. It does not conduct empirical studies, and therefore, no dataset splits (training, validation, or test) are specified. |
| Hardware Specification | No | This is a theoretical paper focused on algorithm design and complexity analysis. It does not report on experiments, and therefore, no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper focused on algorithm design and complexity analysis. It does not report on experiments or provide implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper focused on algorithm design and complexity analysis. It does not conduct experiments and therefore does not include details on experimental setup or hyperparameters. |