Random Permutation Online Isotonic Regression
Authors: Wojciech Kotlowski, Wouter M. Koolen, Alan Malek
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the regret is bounded above by the excess leave-one-out loss for which we develop efficient algorithms and matching lower bounds. We also analyze the class of simple and popular forward algorithms and recommend where to look for algorithms for online isotonic regression on partial orders. |
| Researcher Affiliation | Academia | Wojciech Kotłowski Pozna n University of Technology Poland wkotlowski@cs.put.poznan.pl Wouter M. Koolen Centrum Wiskunde & Informatica Amsterdam, The Netherlands wmkoolen@cwi.nl Alan Malek MIT Cambridge, MA amalek@mit.edu |
| Pseudocode | No | The paper describes algorithms verbally and mathematically, but it does not include structured pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on publicly available datasets for training. It discusses theoretical data models. |
| Dataset Splits | No | The paper does not report experimental results that would involve dataset splits for validation. It is theoretical in nature. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers that would be needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, such as hyperparameters or training configurations. |