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..
Online Estimation via Offline Estimation: An Information-Theoretic Framework
Authors: Dylan J Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Online Estimation via Offline Estimation: An Information-Theoretic Framework. Our main results settle the statistical and computational complexity of online estimation in this framework. Finally, we apply our results to give offline oracle-efficient algorithms for interactive decision making. |
| Researcher Affiliation | Collaboration | Dylan J. Foster EMAIL Yanjun Han EMAIL Jian Qian EMAIL Alexander Rakhlin EMAIL |
| Pseudocode | Yes | Algorithm 1 Version Space Averaging; Algorithm 2 Reduction from OEOE to Online Learning with Delayed Feedback; Algorithm 3 Estimation to Decisions Meta-Algorithm with Offline Oracles (E2D.Off); Algorithm 4 Reduction to delayed online learning for binary loss; Algorithm 5 Reduction from delayed online learning to non-delayed online learning; Algorithm 6 Reduction from CDEw RO to CDE; Algorithm 7 Reduction from CDEw RP to CDEw RO; Algorithm 8 Reduction from CDEw DRP to CDEw RP; Algorithm 9 Reduction from OEOE to CDEw DRP |
| Open Source Code | No | The paper does not contain any statement about making its code open-source or providing a link to a code repository. |
| Open Datasets | No | The paper is theoretical and discusses abstract 'instances' and 'classes' (e.g., '(X, Y, Z, K, F)') without referring to specific, named, or publicly accessible datasets. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments that would require specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not report on experimental setups or hyperparameters. |