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..
On ranking via sorting by estimated expected utility
Authors: Clement Calauzenes, Nicolas Usunier
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By uniformly sampling q 2 Q, we empirically estimated proportions of distributions q vs number of local minima for (q, .), for different numbers of items n. The results are plotted in Fig. 2 (left). To illustrate the claims of this section, we perform simulations using a non-convex surrogate loss defined by smoothing the task loss |
| Researcher Affiliation | Industry | Clément Calauzènes Criteo AI Lab Paris, France EMAIL Nicolas Usunier Facebook AI Research Paris, France EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code. |
| Open Datasets | No | The paper describes using sampled theoretical distributions ('By uniformly sampling q 2 Q, we empirically estimated proportions of distributions q vs number of local minima') for analysis rather than a publicly available dataset for training models. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages or counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | To illustrate the claims of this section, we perform simulations using a non-convex surrogate loss defined by smoothing the task loss... The distributions q are uniformly sampled over Q, rejecting the distributions q where (q, .) does not have any local minima... Fig. 3 (right) show the proportions or runs on these distributions that end up stuck in a bad local valley when using an initialization close to 0 (which empirically was best to avoid bad local valleys). |