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..
Learning Beam Search Policies via Imitation Learning
Authors: Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contributions are: an algorithm for learning beam search policies (Section 4.2) with accompanying regret guarantees (Section 5), a meta-algorithm that captures much of the existing literature (Section 4), and new theoretical results for the early update [6] and La SO [7] algorithms (Section 5.3). |
| Researcher Affiliation | Collaboration | 1Machine Learning Department, Carnegie Mellon University 2Microsoft Research |
| Pseudocode | Yes | Algorithm 1 Beam Search Algorithm 2 Meta-algorithm |
| Open Source Code | No | The paper does not include any statements about releasing code or links to a code repository. |
| Open Datasets | No | The paper defines 'Input-output training pairs D = {(x1, y1), . . . , (xm, ym)}' as part of its theoretical framework, but does not specify any publicly available datasets used for empirical training. |
| Dataset Splits | No | The paper mentions 'return best θt on validation' within its meta-algorithm (Algorithm 2), but does not provide specific details on empirical validation splits or methodology as it is a theoretical paper. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions 'Adam [18]' as an online optimization algorithm but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | No | The paper does not include specific experimental setup details such as hyperparameter values, training configurations, or system-level settings, consistent with its theoretical focus. |