Streaming Algorithms For $\ell_p$ Flows and $\ell_p$ Regression
Authors: Amit Chakrabarti, Jeffrey Jiang, David Woodruff, Taisuke Yasuda
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design streaming algorithms for problem (2), handling a wide range of parameter settings. We also prove several corresponding lower bounds. We consider both the cost estimation problem, where the desired output is a real number that approximates the minimum ℓp norm, as well as the harder vector-valued problem, where the desired output is an approximate minimizer vector x. We work in the setting n d. |
| Researcher Affiliation | Academia | Amit Chakrabarti Department of Computer Science Dartmouth College Hanover, NH 03755, USA EMAIL; Jeffrey Jiang Department of Computer Science Dartmouth College Hanover, NH 03755, USA EMAIL; David P. Woodruff Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA EMAIL; Taisuke Yasuda Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA EMAIL |
| Pseudocode | No | The paper describes algorithms verbally and provides theorems and proofs, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present any code-like structured steps. |
| Open Source Code | No | The paper does not contain any statements about releasing code, links to repositories, or indications that code is provided in supplementary materials. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and lower bounds for mathematical problems (ℓp regression and ℓp flows). It does not involve any empirical studies using specific datasets, therefore no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental evaluation using datasets, thus there is no information about dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and lower bounds. It does not describe any experimental setups or report experimental results, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any implementation or experimental results that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms, theorems, and lower bounds without conducting empirical experiments. Therefore, no experimental setup details like hyperparameters or training configurations are provided. |