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 with Bias and Variance Reduction
Authors: Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaid Harchaoui
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that Prospect can converge 2-3x faster than baselines such as SGD and stochastic saddle-point methods on distribution shift and fairness benchmarks spanning tabular, vision, and language domains. |
| Researcher Affiliation | Collaboration | University of Washington, Google DeepMind, Google Research. |
| Pseudocode | Yes | Algorithm 1 Prospect |
| Open Source Code | Yes | The algorithm implementation and data preparation code is made publicly available online: https://github.com/ronakdm/prospect. |
| Open Datasets | Yes | The datasets used are yacht (n = 244) (Tsanas & Xifara, 2012), energy (n = 614) (Baressi Segota et al., 2020), concrete (n = 824) (Yeh, 2006), kin8nm (n = 6553) (Akujuobi & Zhang, 2017), and power (n = 7654) (T ufekci, 2014). |
| Dataset Splits | Yes | The sample sizes, dimensions, and source of the datasets are summarized in Tab. 2, where d refers to the dimension of each φ(xi).... 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 | No GPUs were used in the study; 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 code used in this project was written in Python 3 using the PyTorch and Numba packages for automatic differentiation and just-in-time compilation, respectively. No specific version numbers for PyTorch or Numba are provided. |
| Experiment Setup | Yes | We fix a minibatch size of 64 SGD and SRDA and an epoch length of N = n for LSVRG... 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. We fix the shift cost ν = 1 and regularization parameter µ = 1/n. |