Learning Sparse Distributions using Iterative Hard Thresholding
Authors: Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi O. Koyejo
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our contributions in this work are algorithmic and theoretical, with proof of concept empirical evaluation. We briefly summarize our contributions below. In addition to our conceptual and theoretical results, we present empirical studies that support our claims. |
| Researcher Affiliation | Academia | Jacky Y. Zhang Department of Computer Science University of Illinois at Urbana-Champaign yiboz@illinois.edu Rajiv Khanna Department of Statistics University of California at Berkeley rajivak@berkeley.edu Anastasios Kyrillidis Department of Computer Science Rice University rajivak@berkeley.edu Oluwasanmi Koyejo Department of Computer Science University of Illinois at Urbana-Champaign sanmi@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Distribution IHT; Algorithm 2 Greedy Sparse Projection (GSProj); Algorithm 3 Greedy Selection |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We study representative prototype selection for the Digits data [31]. |
| Dataset Splits | No | While the paper mentions using a "test dataset" for the Digits data, it does not specify concrete training or validation splits (e.g., exact percentages, sample counts, or clear citations to predefined splits for all partitions). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Initial step size µ = 0.008. |