Online Linear Optimization with Many Hints
Authors: Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work focuses on theoretical foundations. |
| Researcher Affiliation | Collaboration | Aditya Bhaskara Department of Computer Science University of Utah Salt Lake City, UT EMAIL Ashok Cutkosky Dept. of Electrical and Computer Engineering Boston University Boston, MA EMAIL Ravi Kumar Google Research Mountain View, CA EMAIL Manish Purohit Google Research Mountain View, CA EMAIL |
| Pseudocode | Yes | Algorithm 1 K-HINTS |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available, nor does it include a link to a code repository. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical experiments or the use of datasets for training; therefore, there is no mention of publicly available datasets or access information. |
| Dataset Splits | No | As a purely theoretical paper, it does not involve empirical experiments or data partitioning into training, validation, and test sets. |
| Hardware Specification | No | This paper is purely theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | This paper is theoretical and focuses on algorithm design and mathematical proofs; it does not describe any specific software dependencies or versions used for implementation or experimentation. |
| Experiment Setup | No | This paper is purely theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |