On Accelerated Perceptrons and Beyond
Authors: Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob Abernethy
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we unify these existing results under one framework by showing that they can all be described through the lens of solving min-max problems using modern acceleration techniques, mainly through optimistic online learning. We then show that the proposed framework also leads to improved results for a series of problems beyond the standard Perceptron setting. |
| Researcher Affiliation | Collaboration | 1College of Computing, Georgia Tech, Atlanta, GA, USA 2Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA 3Google Research, Atlanta, GA 30309 |
| Pseudocode | Yes | Algorithm 1 Smooth Perceptron (Soheili & Pena, 2012) ... Algorithm 2 Accelerated Perceptron of Ji et al. (2021) ... Algorithm 3 NAG ... Algorithm 4 Accelerated algorithm for the p-norm perceptron |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release, or mention of code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | No | The paper mentions 'a set S of n training examples' but does not provide concrete access information (specific link, DOI, repository name, formal citation, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., 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 | No | The paper discusses algorithmic parameters but does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings for empirical evaluation. |