Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling
Authors: Eric Neyman, Tim Roughgarden
NeurIPS 2023 | Venue PDF | 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 EMAIL Tim Roughgarden Columbia University New York, NY 10027 EMAIL |
| 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. |