Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On Accelerated Perceptrons and Beyond
Authors: Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob Abernethy
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,442 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,643 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |