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..
An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition
Authors: Quan Xiao, Songtao Lu, Tianyi Chen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our stationary measure is a necessary condition of the global optimality. As shown in Figure 1, GALET approaches the global optimal set of Example 1 and our stationary measure also converges to 0, while the value-function based KKT score does not. ... We compare GALET with BOME [37], IAPTT-GM [43] and V-PBGD [57] in the hyper-cleaning task on the MNIST dataset. As shown in Figure 5, GALET converges faster than other methods and the convergence rate of GALET is O(1/K), which matches Theorem 2. Table 2 shows that the test accuracy of GALET is comparable to other methods. |
| Researcher Affiliation | Collaboration | Quan Xiao Rensselaer Polytechnic Institute Troy, NY, USA EMAIL Songtao Lu IBM Research Yorktown Heights, NY, USA EMAIL Tianyi Chen Rensselaer Polytechnic Institute Troy, NY, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 GALET for nonconvex-PL BLO |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We compare GALET with BOME [37], IAPTT-GM [43] and V-PBGD [57] in the hyper-cleaning task on the MNIST dataset. ... We compare our method with the existing methods on the data hyper-cleaning task using the MNIST and the Fashion MNIST dataset [21]. |
| Dataset Splits | Yes | We are given 5000 training data with corruption rate 0.5, 5000 clean validation data and 10000 clean testing data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper mentions computing the Hessian-vector product via an efficient method [53] and using 'auto differentiate', but does not specify any software names with version numbers. |
| Experiment Setup | Yes | Parameter choices. The dimension of the hidden layer of MLP model is set as 50. We select the stepsize from α {1, 10, 50, 100, 200, 500}, γ {0.1, 0.3, 0.5, 0.8} and β {0.001, 0.005, 0.01, 0.05, 0.1}, while the number of loops is chosen from T {5, 10, 20, 30, 50} and N {5, 10, 30, 50, 80}. The default choice of parameter is α = 0.3, K = 30, β = 1, N = 1, γ = 0.1, T = 1. |