No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling
Authors: Eric Neyman, Tim Roughgarden
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an algorithm based on online mirror descent that learns expert weights in a way that attains O(T log T) expected regret as compared with the best weights in hindsight.Our proof has two key ideas. |
| Researcher Affiliation | Academia | Eric Neyman Columbia University New York, NY 10027 eric.neyman@columbia.edu Tim Roughgarden Columbia University New York, NY 10027 tim.roughgarden@gmail.com |
| Pseudocode | Yes | ALGORITHM 1: OMD algorithm for learning weights for logarithmic pooling |
| Open Source Code | No | The paper does not provide any links to open-source code or an explicit statement about releasing the code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not describe specific software dependencies with version numbers for experimental reproduction. |
| Experiment Setup | No | This is a theoretical paper and does not detail an experimental setup with hyperparameters or system-level training settings. |