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..
Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
Authors: Ronak Mehta, Jelena Diakonikolas, Zaid Harchaoui
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical results are supported by numerical benchmarks on regression and classification tasks. |
| Researcher Affiliation | Academia | 1University of Washington, Seattle 2University of Wisconsin, Madison |
| Pseudocode | Yes | Algorithm 1 Distributionally Robust Annular Gradient Optimizer (DRAGO) |
| Open Source Code | Yes | The code to reproduce these experiments can be found at https://github.com/ronakdm/drago. |
| Open Datasets | Yes | We consider regression and classification tasks. Letting (xi, yi) denote a feature-label pair, we have that each ℓi represents the squared error loss or multinomial cross-entropy loss, given by ... yacht (n = 244, d = 6) [Tsanas and Xifara, 2012], energy (n = 614, d = 8) [Baressi Segota et al., 2020], concrete (n = 824, d = 8) [Yeh, 2006], acsincome (n = 4000, d = 202) [Ding et al., 2021], kin8nm (n = 6553, d = 8) [Akujuobi and Zhang, 2017], and power (n = 7654, d = 4) [T ufekci, 2014]. |
| Dataset Splits | Yes | In practice, the regularization parameter µ and shift cost ν are tuned by a statistical metric, i.e. generalization error as measured on a validation set. |
| Hardware Specification | Yes | Experiments were run on a CPU workstation with an Intel i9 processor, a clock speed of 2.80GHz, 32 virtual cores, and 126G of memory. |
| Software Dependencies | No | The paper mentions 'Python 3' and 'Numba packages' for just-in-time compilation, and that algorithms are 'primarily written in PyTorch'. However, specific version numbers are not provided for Numba or PyTorch, making the description not fully reproducible for ancillary software. |
| Experiment Setup | Yes | We fix µ = 1 but vary ν to study its role as a conditioning parameter... We fix a minibatch size of 64 SGD and an epoch length of N = n for LSVRG. For DRAGO, we investigate the variants in which b is set to 1 and b = n/d a priori, as well as cases when b is a tuned hyperparameter... The learning rate η is chosen in the set {1 10 4, 3 10 4, 1 10 3, 3 10 3, 1 10 2, 3 10 2, 1 10 1, 3 10 1, 1 100, 3 100}, with two orders of magnitude lower numbers used in acsincome due to its sparsity. |