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..
Incremental Sampling Without Replacement for Sequence Models
Authors: Kensen Shi, David Bieber, Charles Sutton
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate that using Unique Randomizer leads to higher-quality samples in program synthesis and combinatorial optimization. |
| Researcher Affiliation | Industry | 1Google. Correspondence to: Kensen Shi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Using Unique Randomizer to sample outputs of P without replacement. Algorithm 2: Random choice operation and trie construction for Unique Randomizer. |
| Open Source Code | Yes | Our Python implementations of Unique Randomizer, its batched version, Stochastic Beam Search, and the Hindsight Gumbel Estimator can be found at https://github. com/google-research/unique-randomizer. |
| Open Datasets | Yes | We use the Search-based Pseudocode to Code (SPo C) dataset (Kulal et al., 2019)... |
| Dataset Splits | No | The paper mentions 'test splits' but does not specify training, validation, and test splits with explicit percentages or sample counts, nor does it refer to predefined validation splits. |
| Hardware Specification | No | The paper mentions running experiments 'with a GPU' but does not specify the model, type, or any other detailed hardware specifications. |
| Software Dependencies | No | The paper states 'Our Python implementations' but does not provide specific version numbers for Python or any associated libraries/dependencies. |
| Experiment Setup | Yes | We use a temperature τ = 0.8 when sampling with replacement and τ = 0.4 when sampling without replacement, which we found to be the best among {0.2, 0.4, 0.6, 0.8, 1.0}. For TSP...We set τ = 0.3, 0.2, and 0.15 for n = 20, 50, and 100 nodes, respectively. |