Learning Distributionally Robust Models at Scale via Composite Optimization
Authors: Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, amin karbasi
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide empirical results that demonstrate the effectiveness of our proposed algorithm with respect to the prior art in order to learn robust models from very large datasets. |
| Researcher Affiliation | Collaboration | Farzin Haddadpour Yale Institute for Network Science Yale University farzin.haddadpour@yale.edu Mohammad Mahdi Kamani Wyze Labs Inc. mmkamani@alumni.psu.edu Mehrdad Mahdavi Department of Computer Science & Engineering The Pennsylvania State University mzm616@psu.edu Amin Karbasi Yale Institute for Network Science Yale University amin.karbasi@yale.edu |
| Pseudocode | Yes | Algorithm 1: Generalized Composite Incremental Variance Reduction (GCIVR (x(0))) |
| Open Source Code | Yes | The code for the experiments is available at this repository. http://github.com/haddadpour/composite_optimization |
| Open Datasets | Yes | In this experiment, we use the Adult dataset (Dua & Graff, 2017), and consider race groups of white , black , and other as protected groups. [...] We train a linear classifier with logistic regression, and report the overall error rate of the classifier, as well as the maximum violation of the fairness constraints (equal opportunity) over true group memberships. [...] We set ϵ = 0.05 and the noise level to 0.3. [...] learn a linear classifier with logistic regression to predict the crime rate for a community on Communities and Crime dataset (Dua & Graff, 2017). [...] For this experiment, we use Microsoft Learning to Rank Dataset (MSLR-WEB10K) (Qin & Liu, 2013), which contains 10K queries and 136 features. |
| Dataset Splits | Yes | We use 1000 queries in the training and 100 queries in the test datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train a linear classifier with logistic regression, and report the overall error rate of the classifier, as well as the maximum violation of the fairness constraints (equal opportunity) over true group memberships. [...] We compare with the unconstrained optimization and Heavily-constrained algorithm with a 2-layer neural network, each with 100 nodes as their Lagrange model, as described in their paper. [...] For this experiment we use a non-convex objective, where the model is a two-layer neural network each with 128 nodes and cross-entropy as the loss function. |