Online Linear Optimization with Many Hints
Authors: Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
NeurIPS 2020 | Conference 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 bhaskaraaditya@gmail.com Ashok Cutkosky Dept. of Electrical and Computer Engineering Boston University Boston, MA ashok@cutkosky.com Ravi Kumar Google Research Mountain View, CA ravi.k53@gmail.com Manish Purohit Google Research Mountain View, CA mpurohit@google.com |
| 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. |