Outlier-Robust Distributionally Robust Optimization via Unbalanced Optimal Transport
Authors: Zifan Wang, Yi Shen, Michael Zavlanos, Karl H. Johansson
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide empirical results that demonstrate that our method offers improved robustness to outliers and is computationally less demanding for regression and classification tasks. |
| Researcher Affiliation | Academia | Zifan Wang KTH Royal Institute of Technology zifanw@kth.se Yi Shen Duke University yi.shen478@duke.edu Michael M. Zavlanos Duke University michael.zavlanos@duke.edu Karl H. Johansson KTH Royal Institute of Technology kallej@kth.se |
| Pseudocode | Yes | Algorithm 1: Distributionally robust optimization with outliers |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] Justification: The main contribution of this paper is theoretical. |
| Open Datasets | No | The paper describes synthetic data generation processes (e.g., 'We generate a clean data distribution Pn with n samples, which is uniform over {Xi, θ Xi}n i=1, where X1, . . . , Xn are i.i.d. from X N(0, Id).'). While it references external works for data distribution types, it does not provide concrete access information (links, DOIs, repositories, or direct citations for publicly available datasets used in their experiments). |
| Dataset Splits | No | The paper does not explicitly mention or detail a validation dataset split. It discusses empirical risk minimization and evaluation of excess risk, but without specifying a dedicated validation set. |
| Hardware Specification | Yes | All experiments were conducted on an Intel Core i7-1185G7 CPU (3.00GHz) using Python 3.8. |
| Software Dependencies | Yes | All experiments were conducted on an Intel Core i7-1185G7 CPU (3.00GHz) using Python 3.8. ... We use the stochastic sub-gradient method in Algorithm 1 to solve the Lagrangian penalty problem and the GUROBI [33] solver to solve all other DRO problems we use as benchmarks. |
| Experiment Setup | Yes | We set the parameters as follows: λ = 10, β = 6, λ2 = 5. All the results are averaged over 10 independent runs. We fix ε = 0.1 and C = 8. |