Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data
Authors: Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also empirically demonstrate the performance of our algorithm on the problem of strategic classification with neural networks. We empirically show the performance of the stochastic conditional gradient algorithm on a strategic classification problem in Section 4.1. In this section we illustrate our algorithm on the strategic classification problem as described in Section 1.1 with the Give Me Some Credit4 dataset. |
| Researcher Affiliation | Academia | Abhishek Roy Krishnakumar Balasubramanian Saeed Ghadimi abroy@ucdavis.edu. Halıcıo glu Data Science Institute, University of California, San Diego. Work done while being affiliated with the Department of Statistics, UC Davis. kbala@ucdavis.edu. Department of Statistics, University of California, Davis. sghadimi@uwaterloo.ca. Department of Management Sciences, University of Waterloo. |
| Pseudocode | Yes | Algorithm 1 Inexact Averaged Stochastic Approximation (I-ASA) ... Algorithm 2 Inexact Conditional Gradient (ICG) |
| Open Source Code | Yes | 1. For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper s contributions and scope? [Yes] ... 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | In this section we illustrate our algorithm on the strategic classification problem as described in Section 1.1 with the Give Me Some Credit4 dataset. The main task is a credit score classification problem where the bank (learner) has to decide whether a loan should be granted to a client. 4Available at https://www.kaggle.com/c/Give Me Some Credit/data |
| Dataset Splits | No | The paper mentions selecting a subset of 2000 samples but does not provide specific details on how the dataset was split into training, validation, and test sets (e.g., percentages, exact counts, or cross-validation scheme). |
| Hardware Specification | No | We did not calculate the exact timings. However, our experiments are fairly small-scale ones run on a personal laptop computer, and our main contributions are theoretical. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., Python version, library versions like PyTorch, TensorFlow, or specific solvers with their versions). |
| Experiment Setup | Yes | For this experiment we set n1 = 200. Similar to [LW22], we set α = 0.5λ, and λ = 0.01. For the classifier, the activation function is chosen as sigmoidal, and m = 400. We set N = 20000, and R = 4000. All the parameters of Algorithm 1 are chosen as described in (19). For this experiment we choose d1 = 10, d2 = 20, υ = 0.1, and N = 2000. Rest of the parameters of Algorithm 1 are chosen according to Theorem 3.1. |