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..
Globally Optimal Learning for Structured Elliptical Losses
Authors: Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the empirical appeal of using these losses for regression on synthetic and real-life data. |
| Researcher Affiliation | Collaboration | Yoav Wald Hebrew University EMAIL Nofar Noy Hebrew University EMAIL Ami Wiesel Google Research and Hebrew University EMAIL Gal Elidan Google Research and Hebrew University EMAIL |
| Pseudocode | Yes | Algorithm 1 Minimization Majorization for Elliptical Markov Random Fields |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | Instances of {zi}m i=1 are drawn from multivariate Generalized Gaussian distributions [22] |
| Dataset Splits | Yes | We use data on the years between 2004 and mid-2011 (excluding the mid-2007 to mid-2009 ๏ฌnancial crisis) as training data and test over the values from then until 2015. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions using 'sklearn function make_sparse_psd [23]' but does not provide a specific version number for scikit-learn or any other software dependency. |
| Experiment Setup | No | The paper describes the setup for synthetic and real-life experiments, including data sources and tasks, but it does not provide specific details such as hyperparameters, learning rates, or batch sizes. |