Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Distributionally Robust Optimization via Ball Oracle Acceleration
Authors: Yair Carmon, Danielle Hausler
NeurIPS 2022 | Venue PDF | 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 EMAIL Danielle Hausler Tel Aviv University EMAIL |
| 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. |