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 the Efficient Frontier
Authors: Philippe Chatigny, Ivan Sergienko, Ryan Ferguson, Jordan Weir, Maxime Bergeron
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We used a MC sampling scheme to cover the entire domain in table. 1 uniformly to generate a dataset of approximately one billion samples Dtrain = [(Zinput,1, EF((Zinput,1), ], which we used to train Neural EF in a supervised fashion: ... We generated two test datasets Dtest, Dvalidation of 1 million samples each on the the same domain as the training set described in table 1. |
| Researcher Affiliation | Industry | Philippe Chatigny Riskfuel Toronto EMAIL Ivan Sergienko Beacon Platform New York EMAIL Ryan Ferguson Riskfuel Toronto EMAIL Jordan Weir Riskfuel Toronto EMAIL Maxime Bergeron Riskfuel Toronto EMAIL |
| Pseudocode | Yes | The complete optimal allocation of eq. 3 can be summarized by the following python script: |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the Neural EF model's source code. |
| Open Datasets | No | We used a MC sampling scheme to cover the entire domain in table. 1 uniformly to generate a dataset of approximately one billion samples Dtrain = [(Zinput,1, EF((Zinput,1), ], which we used to train Neural EF in a supervised fashion: ... and All synthetic datasets mimic real-life distributions.... No access information for this generated dataset is provided. |
| Dataset Splits | Yes | We generated two test datasets Dtest, Dvalidation of 1 million samples each on the the same domain as the training set described in table 1. |
| Hardware Specification | Yes | trained on a single NVIDIA A100 GPU with stochastic gradient descent using the Adam W optimizer [34] and the L2 loss. We also used an annealing learning rate decay starting from 5.5e 5 to 1.0e 6. As stated in sec. 2, we also implemented a baseline EF optimization in Py Torch that we used solely for comparing the evaluation throughput (evaluations/seconds) between Neural EF over the base pricer which was implemented using CVXOPT [14]. The hyperparameters of Neural EF are described in table. 2 and were selected by estimated guesses from the accuracy measured on Dvalidation. |
| Software Dependencies | No | implemented using CVXOPT [14], Pytorch. Specific version numbers for these software dependencies are not provided. |
| Experiment Setup | Yes | The hyperparameters of Neural EF are described in table. 2 and were selected by estimated guesses from the accuracy measured on Dvalidation. Table 2: Hyper Parameters of Neural EF. Token dimension 320 Transformer depth 8 Transformer # heads 8 Feed forward projection 1024 Output activation Sigmoid Embedding method [30, 24] Hidden activation Swish [42] and also with stochastic gradient descent using the Adam W optimizer [34] and the L2 loss. We also used an annealing learning rate decay starting from 5.5e 5 to 1.0e 6. |