A Fully First-Order Method for Stochastic Bilevel Optimization
Authors: Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D Nowak
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate even superior practical performance of the proposed method over existing second-order based approaches on MNIST data-hypercleaning experiments. We demonstrate the proposed algorithms on a data hyper-cleaning task involving MNIST (Deng, 2012). |
| Researcher Affiliation | Academia | 1University of Wisconsin-Madison, USA 2University of Seoul, Korea. |
| Pseudocode | Yes | Algorithm 1 F2SA and Algorithm 2 F3SA are included in the paper. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We demonstrate the proposed algorithms on a data hyper-cleaning task involving MNIST (Deng, 2012). |
| Dataset Splits | Yes | We are given a noisy training set Dtrain := {( xi, yi)}n i=1 with the label yi being randomly corrupted with probability p < 1. We are also given a small but clean validation set Dval := {(xi, yi)}m i=1. ... We use n = 19000 training samples and m = 1000 clean validation samples... |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We use n = 19000 training samples and m = 1000 clean validation samples with regularization parameter c = 0.01. We demonstrate the performance of Algorithm 1 (F2SA) and the second-order based method (SOBO) with batch sizes 50 and 500. |